Split (2)

train · 1.28k rows

output bool 2 classes	input stringlengths 345 2.91k
false	Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning physics-aware learning spatiotemporal graph signals few shot learning Modeling the dynamics of real-world physical systems is critical for spatiotemporal prediction tasks, but challenging when data is limited. The scarcity of real-world data and the difficulty in reproducing the data distribution hinder directly applying meta-learning techniques. Although the knowledge of governing partial differential equations (PDE) of the data can be helpful for the fast adaptation to few observations, it is mostly infeasible to exactly find the equation for observations in real-world physical systems. In this work, we propose a framework, physics-aware meta-learning with auxiliary tasks whose spatial modules incorporate PDE-independent knowledge and temporal modules utilize the generalized features from the spatial modules to be adapted to the limited data, respectively. The framework is inspired by a local conservation law expressed mathematically as a continuity equation and does not require the exact form of governing equation to model the spatiotemporal observations. The proposed method mitigates the need for a large number of real-world tasks for meta-learning by leveraging spatial information in simulated data to meta-initialize the spatial modules. We apply the proposed framework to both synthetic and real-world spatiotemporal prediction tasks and demonstrate its superior performance with limited observations.
false	Invisible Traces: Using Hybrid Fingerprinting to identify underlying LLMs in GenAI Apps LLM Fingerprinting AI Security Fingerprinting refers to the process of identifying underlying Machine Learning (ML) models of AI Systems, such as Large Language Models (LLMs), by analyzing their unique characteristics or patterns, much like a human fingerprint. The fingerprinting of Large Language Models (LLMs) has become essential for ensuring the security and transparency of AI-integrated applications. While existing methods primarily rely on access to direct interactions with the application to infer model identity, they often fail in real-world scenarios involving multi-agent systems, frequent model updates, and restricted access to model internals. In this paper, we introduce a novel fingerprinting framework designed to address these challenges by integrating static and dynamic fingerprinting techniques. Our approach identifies architectural features and behavioral traits, enabling accurate and robust fingerprinting of LLMs in dynamic environments. We also highlight new threat scenarios where traditional fingerprinting methods are ineffective. Our results highlight the framework's adaptability to diverse scenarios.
true	Application of Neural Ordinary Differential Equations for Tokamak Plasma Dynamics Analysis Neural ODEs Tokamak Plasma Dynamics Controlled Thermonuclear Fusion Deep Learning Plasma Physics In the quest for controlled thermonuclear fusion, tokamaks present complex challenges in understanding burning plasma dynamics. This study introduces a multi-region multi-timescale transport model, employing Neural Ordinary Differential Equations (Neural ODEs) to simulate the intricate energy transfer processes within tokamaks. Our methodology leverages Neural ODEs for the numerical derivation of diffusivity parameters from DIII-D tokamak experimental data, enabling the precise modeling of energy interactions between electrons and ions across various regions, including the core, edge, and scrape-off layer. These regions are conceptualized as distinct nodes, capturing the critical timescales of radiation and transport processes essential for efficient tokamak operation. Validation against DIII-D plasmas under various auxiliary heating conditions demonstrates the model's effectiveness, ultimately shedding light on ways to enhance tokamak performance with deep learning.
true	Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing Tasks transfer learning contextual embeddings meta embeddings Pre-trained word embeddings are the primary method for transfer learning in several Natural Language Processing (NLP) tasks. Recent works have focused on using unsupervised techniques such as language modeling to obtain these embeddings. In contrast, this work focuses on extracting representations from multiple pre-trained supervised models, which enriches word embeddings with task and domain specific knowledge. Experiments performed in cross-task, cross-domain and crosslingual settings indicate that such supervised embeddings are helpful, especially in the lowresource setting, but the extent of gains is dependent on the nature of the task and domain.
false	AttackDist: Characterizing Zero-day Adversarial Samples by Counter Attack adversarial samples attackdist adversarial attacks dnns defense perturbation counter attack attackdist vulnerable harmfulness Deep Neural Networks (DNNs) have been shown vulnerable to adversarial attacks, which could produce adversarial samples that easily fool the state-of-the-art DNNs. The harmfulness of adversarial attacks calls for the defense mechanisms under fire. However, the relationship between adversarial attacks and defenses is like spear and shield. Whenever a defense method is proposed, a new attack would be followed to bypass the defense immediately. Devising a definitive defense against new attacks~(zero-day attacks) is proven to be challenging. We tackle this challenge by characterizing the intrinsic properties of adversarial samples, via measuring the norm of the perturbation after a counterattack. Our method is based on the idea that, from an optimization perspective, adversarial samples would be closer to the decision boundary; thus the perturbation to counterattack adversarial samples would be significantly smaller than normal cases. Motivated by this, we propose AttackDist, an attack-agnostic property to characterize adversarial samples. We first theoretically clarify under which condition AttackDist can provide a certified detecting performance, then show that a potential application of AttackDist is distinguishing zero-day adversarial examples without knowing the mechanisms of new attacks. As a proof-of-concept, we evaluate AttackDist on two widely used benchmarks. The evaluation results show that AttackDist can outperform the state-of-the-art detection measures by large margins in detecting zero-day adversarial attacks.
false	MACH: Embarrassingly parallel $K$-class classification in $O(d\log{K})$ memory and $O(K\log{K} + d\log{K})$ time, instead of $O(Kd)$ Extreme Classification Large-scale learning hashing GPU High Performance Computing We present Merged-Averaged Classifiers via Hashing (MACH) for $K$-classification with large $K$. Compared to traditional one-vs-all classifiers that require $O(Kd)$ memory and inference cost, MACH only need $O(d\log{K})$ memory while only requiring $O(K\log{K} + d\log{K})$ operation for inference. MACH is the first generic $K$-classification algorithm, with provably theoretical guarantees, which requires $O(\log{K})$ memory without any assumption on the relationship between classes. MACH uses universal hashing to reduce classification with a large number of classes to few independent classification task with very small (constant) number of classes. We provide theoretical quantification of accuracy-memory tradeoff by showing the first connection between extreme classification and heavy hitters. With MACH we can train ODP dataset with 100,000 classes and 400,000 features on a single Titan X GPU (12GB), with the classification accuracy of 19.28\%, which is the best-reported accuracy on this dataset. Before this work, the best performing baseline is a one-vs-all classifier that requires 40 billion parameters (320 GB model size) and achieves 9\% accuracy. In contrast, MACH can achieve 9\% accuracy with 480x reduction in the model size (of mere 0.6GB). With MACH, we also demonstrate complete training of fine-grained imagenet dataset (compressed size 104GB), with 21,000 classes, on a single GPU.
false	Federated Learning's Blessing: FedAvg has Linear Speedup Federated learning Federated learning (FL) learns a model jointly from a set of participating devices without sharing each other's privately held data. The characteristics of non-\textit{i.i.d.} data across the network, low device participation, high communication costs, and the mandate that data remain private bring challenges in understanding the convergence of FL algorithms, particularly in regards to how convergence scales with the number of participating devices. In this paper, we focus on Federated Averaging (FedAvg)--arguably the most popular and effective FL algorithm class in use today--and provide a unified and comprehensive study of its convergence rate. Although FedAvg has recently been studied by an emerging line of literature, it remains open as to how FedAvg's convergence scales with the number of participating devices in the fully heterogeneous FL setting--a crucial question whose answer would shed light on the performance of FedAvg in large FL systems. We fill this gap by providing a unified analysis that establishes convergence guarantees for FedAvg under three classes of problems: strongly convex smooth, convex smooth, and overparameterized strongly convex smooth problems. We show that FedAvg enjoys linear speedup in each case, although with different convergence rates and communication efficiencies. While there have been linear speedup results from distributed optimization that assumes full participation, ours are the first to establish linear speedup for FedAvg under both statistical and system heterogeneity. For strongly convex and convex problems, we also characterize the corresponding convergence rates for the Nesterov accelerated FedAvg algorithm, which are the first linear speedup guarantees for momentum variants of FedAvg in the convex setting. To provably accelerate FedAvg, we design a new momentum-based FL algorithm that further improves the convergence rate in overparameterized linear regression problems. Empirical studies of the algorithms in various settings have supported our theoretical results.
false	BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS federated learning secure machine learning backdoor attacks inference attacks data privacy Recently, federated learning (FL) has been subject to both security and privacy attacks posing a dilemmatic challenge on the underlying algorithmic designs: On the one hand, FL is shown to be vulnerable to backdoor attacks that stealthily manipulate the global model output using malicious model updates, and on the other hand, FL is shown vulnerable to inference attacks by a malicious aggregator inferring information about clients’ data from their model updates. Unfortunately, existing defenses against these attacks are insufficient and mitigating both attacks at the same time is highly challenging, because while defeating backdoor attacks requires the analysis of model updates, protection against inference attacks prohibits access to the model updates to avoid information leakage. In this work, we introduce BAFFLE, a novel in-depth defense for FL that tackles this challenge. To mitigate backdoor attacks, it applies a multilayered defense by using a Model Filtering layer to detect and reject malicious model updates and a Poison Elimination layer to eliminate any effect of a remaining undetected weak manipulation. To impede inference attacks, we build private BAFFLE that securely evaluates the BAFFLE algorithm under encryption using sophisticated secure computation techniques. We extensively evaluate BAFFLE against state-of-the-art backdoor attacks on several datasets and applications, including image classification, word prediction, and IoT intrusion. We show that BAFFLE can entirely remove backdoors with a negligible effect on accuracy and that private BAFFLE is practical.
true	FVD: A new Metric for Video Generation Metric Evaluation Video Generation Generative Models Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images. Following their successful application in image processing and representation learning, an important next step is to consider videos. Learning generative models of video is a much harder task, requiring a model to capture the temporal dynamics of a scene, in addition to the visual presentation of objects. While recent generative models of video have had some success, current progress is hampered by the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples. To this extent we propose Fréchet Video Distance (FVD), a new metric for generative models of video based on FID. We contribute a large-scale human study, which confirms that FVD correlates well with qualitative human judgment of generated videos.
false	Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference msign bayesian inference problems posterior superior performance bayesian inference challenge samples multiple modes High-dimensional Bayesian inference problems cast a long-standing challenge in generating samples, especially when the posterior has multiple modes. For a wide class of Bayesian inference problems equipped with the multiscale structure that low-dimensional (coarse-scale) surrogate can approximate the original high-dimensional (fine-scale) problem well, we propose to train a Multiscale Invertible Generative Network (MsIGN) for sample generation. A novel prior conditioning layer is designed to bridge networks at different resolutions, enabling coarse-to-fine multi-stage training. Jeffreys divergence is adopted as the training objective to avoid mode dropping. On two high-dimensional Bayesian inverse problems, MsIGN approximates the posterior accurately and clearly captures multiple modes, showing superior performance compared with previous deep generative network approaches. On the natural image synthesis task, MsIGN achieves the superior performance in bits-per-dimension compared with our baseline models and yields great interpret-ability of its neurons in intermediate layers.
true	Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds Audio-visual sound separation in-the-wild data unsupervised learning self-supervised learning universal sound separation Recent progress in deep learning has enabled many advances in sound separation and visual scene understanding. However, extracting sound sources which are apparent in natural videos remains an open problem. In this work, we present AudioScope, a novel audio-visual sound separation framework that can be trained without supervision to isolate on-screen sound sources from real in-the-wild videos. Prior audio-visual separation work assumed artificial limitations on the domain of sound classes (e.g., to speech or music), constrained the number of sources, and required strong sound separation or visual segmentation labels. AudioScope overcomes these limitations, operating on an open domain of sounds, with variable numbers of sources, and without labels or prior visual segmentation. The training procedure for AudioScope uses mixture invariant training (MixIT) to separate synthetic mixtures of mixtures (MoMs) into individual sources, where noisy labels for mixtures are provided by an unsupervised audio-visual coincidence model. Using the noisy labels, along with attention between video and audio features, AudioScope learns to identify audio-visual similarity and to suppress off-screen sounds. We demonstrate the effectiveness of our approach using a dataset of video clips extracted from open-domain YFCC100m video data. This dataset contains a wide diversity of sound classes recorded in unconstrained conditions, making the application of previous methods unsuitable. For evaluation and semi-supervised experiments, we collected human labels for presence of on-screen and off-screen sounds on a small subset of clips.
true	Variational Information Bottleneck for Effective Low-Resource Fine-Tuning Transfer learning NLP large-scale pre-trained language models over-fitting robust biases variational information bottleneck While large-scale pretrained language models have obtained impressive results when fine-tuned on a wide variety of tasks, they still often suffer from overfitting in low-resource scenarios. Since such models are general-purpose feature extractors, many of these features are inevitably irrelevant for a given target task. We propose to use Variational Information Bottleneck (VIB) to suppress irrelevant features when fine-tuning on low-resource target tasks, and show that our method successfully reduces overfitting. Moreover, we show that our VIB model finds sentence representations that are more robust to biases in natural language inference datasets, and thereby obtains better generalization to out-of-domain datasets. Evaluation on seven low-resource datasets in different tasks shows that our method significantly improves transfer learning in low-resource scenarios, surpassing prior work. Moreover, it improves generalization on 13 out of 15 out-of-domain natural language inference benchmarks. Our code is publicly available in https://github.com/rabeehk/vibert.
true	Mind the Gap: A Practical Attack on GGUF Quantization quantization large language models security poisoning gguf With the increasing size of frontier LLMs, post-training quantization has become the standard for memory-efficient deployment. Recent work has shown that basic rounding-based quantization schemes pose security risks, as they can be exploited to inject malicious behaviors into quantized models that remain hidden in full precision. However, existing attacks cannot be applied to more complex quantization methods, such as the GGUF family used in the popular ollama and llama.cpp frameworks. In this work, we address this gap by introducing the first attack on GGUF. Our key insight is that the quantization error -- the difference between the full-precision weights and their (de-)quantized version -- provides sufficient flexibility to construct malicious quantized models that appear benign in full precision. Leveraging this, we develop an attack that trains the target malicious LLM while constraining its weights based on quantization errors. We demonstrate the effectiveness of our attack on three popular LLMs across nine GGUF quantization data types on three diverse attack scenarios: insecure code generation ($\Delta$=$88.7\%$), targeted content injection ($\Delta$=$85.0\%$), and benign instruction refusal ($\Delta$=$30.1\%$). Our attack highlights that (1) the most widely used post-training quantization method is susceptible to adversarial interferences, and (2) the complexity of quantization schemes alone is insufficient as a defense.
true	Fast And Slow Learning Of Recurrent Independent Mechanisms modular representations better generalization learning mechanisms Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning agent interacting with its environment is likely to be faced with situations requiring novel combinations of existing pieces of knowledge. We hypothesize that such a decomposition of knowledge is particularly relevant for being able to generalize in a systematic way to out-of-distribution changes. To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs and its reward function are stationary and can be re-used across tasks. An attention mechanism dynamically selects which modules can be adapted to the current task, and the parameters of the \textit{selected} modules are allowed to change quickly as the learner is confronted with variations in what it experiences, while the parameters of the attention mechanisms act as stable, slowly changing, meta-parameters. We focus on pieces of knowledge captured by an ensemble of modules sparsely communicating with each other via a bottleneck of attention. We find that meta-learning the modular aspects of the proposed system greatly helps in achieving faster adaptation in a reinforcement learning setup involving navigation in a partially observed grid world with image-level input. We also find that reversing the role of parameters and meta-parameters does not work nearly as well, suggesting a particular role for fast adaptation of the dynamically selected modules.
false	Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement Learning Imitation Learning Reinforcement Learning Universal Value Functions This work considers two distinct settings: imitation learning and goal-conditioned reinforcement learning. In either case, effective solutions require the agent to reliably reach a specified state (a goal), or set of states (a demonstration). Drawing a connection between probabilistic long-term dynamics and the desired value function, this work introduces an approach that utilizes recent advances in density estimation to effectively learn to reach a given state. We develop a unified view on the two settings and show that the approach can be applied to both. In goal-conditioned reinforcement learning, we show it to circumvent the problem of sparse rewards while addressing hindsight bias in stochastic domains. In imitation learning, we show that the approach can learn from extremely sparse amounts of expert data and achieves state-of-the-art results on a common benchmark.
true	HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark Hardware-Aware Neural Architecture Search AutoML Benchmark HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous attention by automating the design of deep neural networks deployed in more resource-constrained daily life devices. Despite its promising performance, developing optimal HW-NAS solutions can be prohibitively challenging as it requires cross-disciplinary knowledge in the algorithm, micro-architecture, and device-specific compilation. First, to determine the hardware-cost to be incorporated into the NAS process, existing works mostly adopt either pre-collected hardware-cost look-up tables or device-specific hardware-cost models. The former can be time-consuming due to the required knowledge of the device’s compilation method and how to set up the measurement pipeline, while building the latter is often a barrier for non-hardware experts like NAS researchers. Both of them limit the development of HW-NAS innovations and impose a barrier-to-entry to non-hardware experts. Second, similar to generic NAS, it can be notoriously difficult to benchmark HW-NAS algorithms due to their significant required computational resources and the differences in adopted search spaces, hyperparameters, and hardware devices. To this end, we develop HW-NAS-Bench, the first public dataset for HW-NAS research which aims to democratize HW-NAS research to non-hardware experts and make HW-NAS research more reproducible and accessible. To design HW-NAS-Bench, we carefully collected the measured/estimated hardware performance (e.g., energy cost and latency) of all the networks in the search spaces of both NAS-Bench-201 and FBNet, on six hardware devices that fall into three categories (i.e., commercial edge devices, FPGA, and ASIC). Furthermore, we provide a comprehensive analysis of the collected measurements in HW-NAS-Bench to provide insights for HW-NAS research. Finally, we demonstrate exemplary user cases to (1) show that HW-NAS-Bench allows non-hardware experts to perform HW-NAS by simply querying our pre-measured dataset and (2) verify that dedicated device-specific HW-NAS can indeed lead to optimal accuracy-cost trade-offs. The codes and all collected data are available at https://github.com/RICE-EIC/HW-NAS-Bench.
true	Spatially Structured Recurrent Modules spatio-temporal modelling modular architectures recurrent neural networks partially observed environments Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalise well and are robust to changes in the input distribution. While methods that harness spatial and temporal structures find broad application, recent work has demonstrated the potential of models that leverage sparse and modular structure using an ensemble of sparingly interacting modules. In this work, we take a step towards dynamic models that are capable of simultaneously exploiting both modular and spatiotemporal structures. To this end, we model the dynamical system as a collection of autonomous but sparsely interacting sub-systems that interact according to a learned topology which is informed by the spatial structure of the underlying system. This gives rise to a class of models that are well suited for capturing the dynamics of systems that only offer local views into their state, along with corresponding spatial locations of those views. On the tasks of video prediction from cropped frames and multi-agent world modelling from partial observations in the challenging Starcraft2 domain, we find our models to be more robust to the number of available views and better capable of generalisation to novel tasks without additional training than strong baselines that perform equally well or better on the training distribution.
true	Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation causal inference representation learning individualized treatment effect estimation State-of-the-art methods for conditional average treatment effect (CATE) estimation make widespread use of representation learning. Here, the idea is to reduce the variance of the low-sample CATE estimation by a (potentially constrained) low-dimensional representation. However, low-dimensional representations can lose information about the observed confounders and thus lead to bias, because of which the validity of representation learning for CATE estimation is typically violated. In this paper, we propose a new, representation-agnostic refutation framework for estimating bounds on the representation-induced confounding bias that comes from dimensionality reduction (or other constraints on the representations) in CATE estimation. First, we establish theoretically under which conditions CATE is non-identifiable given low-dimensional (constrained) representations. Second, as our remedy, we propose a neural refutation framework which performs partial identification of CATE or, equivalently, aims at estimating lower and upper bounds of the representation-induced confounding bias. We demonstrate the effectiveness of our bounds in a series of experiments. In sum, our refutation framework is of direct relevance in practice where the validity of CATE estimation is of importance.
true	Building Bridges, Not Walls: Advancing Interpretability by Unifying Feature, Data, and Model Component Attribution Interpretability Attribution Explainability The increasing complexity of AI systems has made understanding their behavior and building trust in them a critical challenge, especially for large language models. Numerous methods have been developed to attribute model behavior to three key aspects: input features, training data, and internal model components. However, these attribution methods are studied and applied rather independently, resulting in a fragmented landscape of approaches and terminology. We argues that feature, data, and component attribution methods share fundamental similarities, and bridging them can benefit interpretability research. We conduct a detailed analysis of successful methods of these three attribution aspects and present a unified view to demonstrate that they employ similar approaches: perturbations, gradients, and linear approximations. Our unified view enhances understanding of attribution methods and highlights new directions for interpretability and broader AI areas, including model editing, steering, and regulation.
true	Unnatural Languages Are Not Bugs but Features for LLMs Unnatural Languages Large Language Models Large Language Models (LLMs) have been observed to process non-human-readable text sequences, such as jailbreak prompts, often viewed as a bug for aligned LLMs. In this work, we present a systematic investigation challenging this perception, demonstrating that unnatural languages - strings that appear incomprehensible to humans but maintain semantic meanings for LLMs - contain latent features usable by models. Notably, unnatural languages possess latent features that can be generalized across different models and tasks during inference. Furthermore, models fine-tuned on unnatural versions of instruction datasets perform on-par with those trained on natural language, achieving (49.71) win rates in Length-controlled AlpacaEval 2.0 in average across various base models. In addition, through comprehensive analysis, we demonstrate that LLMs process unnatural languages by filtering noise and inferring contextual meaning from filtered words.
true	Chaos of Learning Beyond Zero-sum and Coordination via Game Decompositions Learning in Games Lyapunov Chaos Game Decomposition Multiplicative Weights Update Follow-the-Regularized-Leader Volume Analysis Dynamical Systems It is of primary interest for ML to understand how agents learn and interact dynamically in competitive environments and games (e.g. GANs). But this has been a difficult task, as irregular behaviors are commonly observed in such systems. This can be explained theoretically, for instance, by the works of Cheung and Piliouras (COLT 2019; NeurIPS 2020), which showed that in two-person zero-sum games, if agents employ one of the most well-known learning algorithms, Multiplicative Weights Update (MWU), then Lyapunov chaos occurs everywhere in the payoff space. In this paper, we study how persistent chaos can occur in the more general normal game settings, where the agents might have the motivation to coordinate (which is not true for zero-sum games) and the number of agents can be arbitrary. We characterize bimatrix games where MWU, its optimistic variant (OMWU) or Follow-the-Regularized-Leader (FTRL) algorithms are Lyapunov chaotic almost everywhere in the payoff space. Technically, our characterization is derived by extending the volume-expansion argument of Cheung and Piliouras via the canonical game decomposition into zero-sum and coordination components. Interestingly, the two components induce opposite volume-changing behaviors, so the overall behavior can be analyzed by comparing the strengths of the components against each other. The comparison is done via our new notion of "matrix domination" or via a linear program. For multi-player games, we present a local equivalence of volume change between general games and graphical games, which is used to perform volume and chaos analyses of MWU and OMWU in potential games.
false	Implicit Regularization of SGD via Thermophoresis SGD regularization generalization statistical mechanics thermophoresis A central ingredient in the impressive predictive performance of deep neural networks is optimization via stochastic gradient descent (SGD). While some theoretical progress has been made, the effect of SGD in neural networks is still unclear, especially during the early phase of training. Here we generalize the theory of thermophoresis from statistical mechanics and show that there exists an effective entropic force from SGD that pushes to reduce the gradient variance. We study this effect in detail in a simple two-layer model, where the thermophoretic force functions to decreases the weight norm and activation rate of the units. The strength of this effect is proportional to squared learning rate and inverse batch size, and is more effective during the early phase of training when the model's predictions are poor. Lastly we test our quantitative predictions with experiments on various models and datasets.
false	Learning to select examples for program synthesis program synthesis program induction example selection Program synthesis is a class of regression problems where one seeks a solution, in the form of a source-code program, that maps the inputs to their corresponding outputs exactly. Due to its precise and combinatorial nature, it is commonly formulated as a constraint satisfaction problem, where input-output examples are expressed constraints, and solved with a constraint solver. A key challenge of this formulation is that of scalability: While constraint solvers work well with few well-chosen examples, constraining the entire set of example constitutes a significant overhead in both time and memory. In this paper we address this challenge by constructing a representative subset of examples that is both small and is able to constrain the solver sufficiently. We build the subset one example at a time, using a trained discriminator to predict the probability of unchosen input-output examples conditioned on the chosen input-output examples, adding the least probable example to the subset. Experiment on a diagram drawing domain shows our approach produces subset of examples that are small and representative for the constraint solver.
true	Towards Understanding Distilled Reasoning Models: A Representational Approach Interpretability Reasoning Model Distillation Sparse Crosscoder In this paper, we investigate how model distillation impacts the development of reasoning features in large language models (LLMs). To explore this, we train a crosscoder on Qwen-series models and their fine-tuned variants. Our results suggest that the crosscoder learns features corresponding to various types of reasoning, including self-reflection and computation verification. Moreover, we observe that distilled models contain unique reasoning feature directions, which could be used to steer the model into over-thinking or incisive-thinking mode. In particular, we perform analysis on four specific reasoning categories: (a) self-reflection, (b) deductive reasoning, (c) alternative reasoning, and (d) contrastive reasoning. Finally, we examine the changes in feature geometry resulting from the distillation process and find indications that larger distilled models may develop more structured representations, which correlate with enhanced distillation performance. By providing insights into how distillation modifies the model, our study contributes to enhancing the transparency and reliability of AI systems.
false	Exploiting Verified Neural Networks via Floating Point Numerical Error point numerical error verified neural networks deep neural networks respect verification system need robustness researchers verification algorithms neural network Motivated by the need to reliably characterize the robustness of deep neural networks, researchers have developed verification algorithms for deep neural networks. Given a neural network, the verifiers aim to answer whether certain properties are guaranteed with respect to all inputs in a space. However, little attention has been paid to floating point numerical error in neural network verification. We exploit floating point errors in the inference and verification implementations to construct adversarial examples for neural networks that a verifier claims to be robust with respect to certain inputs. We argue that, to produce sound verification results, any verification system must accurately (or conservatively) model the effects of any float point computations in the network inference or verification system.
true	Coherence Evaluation of Visual Concepts With Objects and Language Visual Concepts Interpretability Objects Vision-Language modeling Conceptual Explanations Meaningful concepts are the fundamental elements of human reasoning. In explainable AI, they are used to provide concept-based explanations of machine learning models. The concepts are often extracted from large-scale image data sets in an unsupervised manner and are therefore not guaranteed to be meaningful to users. In this work, we investigate to which extent we can automatically assess the meaningfulness of such visual concepts using objects and language as forms of supervision. On the way towards discovering more interpretable concepts, we propose the “Semantic-level, Object and Language-Guided Coherence Evaluation” framework for visual concepts (SOLaCE). SOLaCE assigns semantic meanings in the form of words to automatically discovered visual concepts and evaluates their degree of meaningfulness on this higher level without human effort. We consider the question of whether objects are sufﬁcient as possible meanings, or whether a broader vocabulary including more abstract meanings needs to be considered. By means of a user study, we conﬁrm that our simulated evaluations highly agree with the human perception of coherence. We publicly release our data set containing 2600 human ratings of visual concepts.
false	Learning Contextual Perturbation Budgets for Training Robust Neural Networks adversarial robustness certified robustness certfied robust training Existing methods for training robust neural networks generally aim to make models uniformly robust on all input dimensions. However, different input dimensions are not uniformly important to the prediction. In this paper, we propose a novel framework to train certifiably robust models and learn non-uniform perturbation budgets on different input dimensions, in contrast to using the popular $\ell_\infty$ threat model. We incorporate a perturbation budget generator into the existing certified defense framework, and perform certified training with generated perturbation budgets. In comparison to the radius of $\ell_\infty$ ball in previous works, the robustness intensity is measured by robustness volume which is the multiplication of perturbation budgets on all input dimensions. We evaluate our method on MNIST and CIFAR-10 datasets and show that we can achieve lower clean and certified errors on relatively larger robustness volumes, compared to methods using uniform perturbation budgets. Further with two synthetic datasets constructed from MNIST and CIFAR-10, we also demonstrate that the perturbation budget generator can produce semantically-meaningful budgets, which implies that the generator can capture contextual information and the sensitivity of different features in input images.
false	Success-Rate Targeted Reinforcement Learning by Disorientation Penalty reinforcement learning undiscounted return success rate Current reinforcement learning generally uses discounted return as its learning objective. However, real-world tasks may often demand a high success rate, which can be quite different from optimizing rewards. In this paper, we explicitly formulate the success rate as an undiscounted form of return with {0, 1}-binary reward function. Unfortunately, applying traditional Bellman updates to value function learning can be problematic for learning undiscounted return, and thus not suitable for optimizing success rate. From our theoretical analysis, we discover that values across different states tend to converge to the same value, resulting in the agent wandering around those states without making any actual progress. This further leads to reduced learning efficiency and inability to complete a task in time. To combat the aforementioned issue, we propose a new method, which introduces Loop Penalty (LP) into value function learning, to penalize disoriented cycling behaviors in the agent's decision-making. We demonstrate the effectiveness of our proposed LP on three environments, including grid-world cliff-walking, Doom first-person navigation and robot arm control, and compare our method with Q-learning, Monte-Carlo and Proximal Policy Optimization (PPO). Empirically, LP improves the convergence of training and achieves a higher success rate.
true	Perceptual Generative Autoencoders pga latent space maximum likelihood vae target distributions data space intrinsic dimensionality data lower Modern generative models are usually designed to match target distributions directly in the data space, where the intrinsic dimensionality of data can be much lower than the ambient dimensionality. We argue that this discrepancy may contribute to the difficulties in training generative models. We therefore propose to map both the generated and target distributions to the latent space using the encoder of a standard autoencoder, and train the generator (or decoder) to match the target distribution in the latent space. The resulting method, perceptual generative autoencoder (PGA), is then incorporated with maximum likelihood or variational autoencoder (VAE) objective to train the generative model. With maximum likelihood, PGA generalizes the idea of reversible generative models to unrestricted neural network architectures and arbitrary latent dimensionalities. When combined with VAE, PGA can generate sharper samples than vanilla VAE.
false	Approximate Probabilistic Inference with Composed Flows normalizing flow probabilistic inference variational inference inverse problem We study the problem of probabilistic inference on the joint distribution defined by a normalizing flow model. Given a pre-trained flow model $p(\boldsymbol{x})$, we wish to estimate $p(\boldsymbol{x}_2 \mid \boldsymbol{x}_1)$ for some arbitrary partitioning of the variables $\boldsymbol{x} = (\boldsymbol{x}_1, \boldsymbol{x}_2)$. We first show that this task is computationally hard for a large class of flow models. Motivated by this hardness result, we propose a framework for $\textit{approximate}$ probabilistic inference. Specifically, our method trains a new generative model with the property that its composition with the given model approximates the target conditional distribution. By parametrizing this new distribution as another flow model, we can efficiently train it using variational inference and also handle conditioning under arbitrary differentiable transformations. Since the resulting approximate posterior remains a flow, it offers exact likelihood evaluation, inversion, and efficient sampling. We provide an extensive empirical evidence showcasing the flexibility of our method on a variety of inference tasks with applications to inverse problems. We also experimentally demonstrate that our approach is comparable to simple MCMC baselines in terms of sample quality. Further, we explain the failure of naively applying variational inference and show that our method does not suffer from the same issue.
false	VilNMN: A Neural Module Network approach to Video-Grounded Language Tasks neural modular networks video-grounded dialogues dialogue understanding video understanding video QA video-grounded language tasks Neural module networks (NMN) have achieved success in image-grounded tasks such as question answering (QA) on synthetic images. However, very limited work on NMN has been studied in the video-grounded language tasks. These tasks extend the complexity of traditional visual tasks with the additional visual temporal variance. Motivated by recent NMN approaches on image-grounded tasks, we introduce Visio-Linguistic Neural Module Network (VilNMN) to model the information retrieval process in video-grounded language tasks as a pipeline of neural modules. VilNMN first decomposes all language components to explicitly resolves entity references and detect corresponding action-based inputs from the question. Detected entities and actions are used as parameters to instantiate neural module networks and extract visual cues from the video. Our experiments show that VilNMN can achieve promising performance on two video-grounded language tasks: video QA and video-grounded dialogues.
false	Enabling Binary Neural Network Training on the Edge Binary neural network edge computing neural network training The ever-growing computational demands of increasingly complex machine learning models frequently necessitate the use of powerful cloud-based infrastructure for their training. Binary neural networks are known to be promising candidates for on-device inference due to their extreme compute and memory savings over higher-precision alternatives. In this paper, we demonstrate that they are also strongly robust to gradient quantization, thereby making the training of modern models on the edge a practical reality. We introduce a low-cost binary neural network training strategy exhibiting sizable memory footprint reductions and energy savings vs Courbariaux & Bengio's standard approach. Against the latter, we see coincident memory requirement and energy consumption drops of 2--6$\times$, while reaching similar test accuracy, across a range of small-scale models trained to classify popular datasets. We also showcase ImageNet training of ResNetE-18, achieving a 3.12$\times$ memory reduction over the aforementioned standard. Such savings will allow for unnecessary cloud offloading to be avoided, reducing latency and increasing energy efficiency while also safeguarding user privacy.
false	Variational Inference for Laser Disturbance Detection in Powder Bed Fusion Variational Inference Machine Learning Additive Manufacturing 3D Printing Dynamics In this study we use variational inference to learn a dynamics model from a high-speed video stream of a laser melting process. We compare two deep generative sequence models and evaluate them on video prediction and anomaly detection tasks. We find that the latent representation provides sufficient robustness to detect anomalies to high levels of performance (AUROC=0.9999). The method is generally applicable to high dimensional time-series modelling and distils the temporal data-stream to a single metric.
false	Flexible Prior Distributions for Deep Generative Models Deep Generative Models GANs We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simple priors over codes with complex deterministic models, we argue that it might be advantageous to use more flexible code distributions. We demonstrate how these distributions can be induced directly from the data. The benefits include: more powerful generative models, better modeling of latent structure and explicit control of the degree of generalization.
true	A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision Multi-Label Learning Weakly-Supervised Learning Pseudo-Labels Meta-Learning The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts. Previous works of MLL mainly focused on the setting where the concept set is assumed to be fixed, while many real-world applications require introducing new concepts into the set to meet new demands. One common need is to refine the original coarse concepts and split them into finer-grained ones, where the refinement process typically begins with limited labeled data for the finer-grained concepts. To address the need, we propose a special weakly supervised MLL problem that not only focuses on the situation of limited fine-grained supervision but also leverages the hierarchical relationship between the coarse concepts and the fine-grained ones. The problem can be reduced to a multi-label version of negative-unlabeled learning problem using the hierarchical relationship. We tackle the reduced problem with a meta-learning approach that learns to assign pseudo-labels to the unlabeled entries. Experimental results demonstrate that our proposed method is able to assign accurate pseudo-labels, and in turn achieves superior classification performance when compared with other existing methods.
true	Equivariant Neural Fields For Symmetry Preserving Continous PDE Forecasting Equivariance Neural Field PDE Forecasting GNN Neural ODE Recently, Neural Fields (NeFs) have emerged as a powerful modelling paradigm to represent discretely-sampled continuous signals. As such, novel work has explored the use of Conditional NeFs to model PDEs, by learning continuous flows in the latent space of the Conditional NeF. Although this approach benefits from favourable properties of neural fields such as grid-agnosticity and space-time-continuous dynamics modelling, it does not make use of important geometric information about the domain of the PDE being modelled -- such as information on symmetries of the PDE -- in favour of modelling flexibility. Instead, we propose a NeF parameterization that preserves geometric information in the latent space of the Conditional NeF: \textit{Equivariant Neural Fields}. Using this representation, we construct a framework for space-time continuous PDE modelling that preserves known symmetries of the PDE. We experimentally validate our model and show it readily generalizes to arbitrary locations, as well as geometric transformations of the initial conditions - where other NeF-based PDE forecasting methods fail.
true	The Implicit Bias of Gradient Descent on Separable Data gradient descent implicit regularization generalization margin logistic regression loss functions optimization exponential tail cross-entropy We show that gradient descent on an unregularized logistic regression problem, for almost all separable datasets, converges to the same direction as the max-margin solution. The result generalizes also to other monotone decreasing loss functions with an infimum at infinity, and we also discuss a multi-class generalizations to the cross entropy loss. Furthermore, we show this convergence is very slow, and only logarithmic in the convergence of the loss itself. This can help explain the benefit of continuing to optimize the logistic or cross-entropy loss even after the training error is zero and the training loss is extremely small, and, as we show, even if the validation loss increases. Our methodology can also aid in understanding implicit regularization in more complex models and with other optimization methods.
true	Investigation of Numerical Diffusion in Aerodynamic Flow Simulations with Physics Informed Neural Networks Numerical Diffusion Aerodynamic Flows Physics Informed Neural Networks Computational Fluid Dynamics (CFD) simulations are used for many air ﬂow simulations including road vehicle aerodynamics. Numerical diffusion occurs when local ﬂow direction is not aligned with the mesh lines and when there is a non-zero gradient of the dependent variable in the direction normal to the streamline direction. It has been observed that typical numerical discretization schemes for the Navier-Stokes equations such as ﬁrst order upwinding produce very accurate solutions without numerical diffusion when the mesh is aligned with the streamline direction. On the other hand, numerical diffusion is maximized when the streamline direction is at an angle of 45° relative to the mesh line. Numerical diffusion can be reduced by mesh reﬁnements such as aligning mesh lines along the local ﬂow direction or by introducing higher order numerical schemes, which may introduce potential numerical instability or additional computational cost. A few test cases of a simple steady-state incompressible and inviscid air ﬂow convection problem were used to investigate whether numerical diffusion occurs when using Physics Informed Neural Networks (PINNs) that rely on automatic differentiation as opposed to numerical techniques used in traditional CFD solvers. Numerical diffusion was not observed when PINNs were used to solve the partial differential equation (PDE) for the simple convection problem irrespective of ﬂow angle. The PINN correctly simulated the streamwise upwinding, which has great potential to improve the accuracy of Navier-Stokes solvers.
true	Model Evaluations Need Rigorous and Transparent Human Baselines human baseline human performance human performance baseline science of evaluations AI evaluation model evaluation LLM evaluation evaluation methodology language model foundation model This position paper argues that human baselines in foundation model evaluations must be more rigorous and more transparent to enable meaningful comparisons of human vs. AI performance. Human performance baselines are vital for the machine learning community, downstream users, and policymakers to interpret AI evaluations. Models are often claimed to achieve "super-human" performance, but existing baselining methods are neither sufficiently rigorous nor sufficiently well-documented to robustly measure and assess performance differences. Based on a meta-review of the measurement theory and AI evaluation literatures, we derive a framework for assessing human baselining methods. We then use our framework to systematically review 113 human baselines (studies) in foundation model evaluations, identifying shortcomings in existing baselining methods. We publish our framework as a reporting checklist for researchers conducting human baseline studies. We hope our work can advance more rigorous AI evaluation practices that can better serve both the research community and policymakers.
true	Score-Based Generative Modeling through Stochastic Differential Equations generative models score-based generative models stochastic differential equations score matching diffusion Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (a.k.a., score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of $1024\times 1024$ images for the first time from a score-based generative model.
false	Sparse Gaussian Process Variational Autoencoders Gaussian process variational inference variational autoencoders Bayesian inference Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering. An effective framework for handling such data are Gaussian process deep generative models (GP-DGMs), which employ GP priors over the latent variables of DGMs. Existing approaches for performing inference in GP-DGMs do not support sparse GP approximations based on inducing points, which are essential for the computational efficiency of GPs, nor do they handle missing data -- a natural occurrence in many spatio-temporal datasets -- in a principled manner. We address these shortcomings with the development of the sparse Gaussian process variational autoencoder (SGP-VAE), characterised by the use of partial inference networks for parameterising sparse GP approximations. Leveraging the benefits of amortised variational inference, the SGP-VAE enables inference in multi-output sparse GPs on previously unobserved data with no additional training. The SGP-VAE is evaluated in a variety of experiments where it outperforms alternative approaches including multi-output GPs and structured VAEs.
false	Mitigating Deep Double Descent by Concatenating Inputs deep double descent feedforward neural network image classificaiton The double descent curve is one of the most intriguing properties of deep neural networks. It contrasts the classical bias-variance curve with the behavior of modern neural networks, occurring where the number of samples nears the number of parameters. In this work, we explore the connection between the double descent phenomena and the number of samples in the deep neural network setting. In particular, we propose a construction which augments the existing dataset by artificially increasing the number of samples. This construction empirically mitigates the double descent curve in this setting. We reproduce existing work on deep double descent, and observe a smooth descent into the overparameterized region for our construction. This occurs both with respect to the model size, and with respect to the number epochs.
false	WebGauntlet: Measuring Instruction Following and Robustness for Web Agents Language Agents Benchmarks Web Agents AI Safety Robustness Recent advances in language model (LM) agents and tool calling have enabled autonomous, iterative systems to emulate digital behavior in a variety of environments. In order to better understand the instruction following limitations of LM agents, we introduce WebGauntlet, a benchmark that stress tests the robustness of web agents in realistic online environments. Our environment replicates online e-commerce settings for agents to traverse and perform simple tasks for users. Our threat model concretizes dozens of environment-side attacks and finds that LM agents struggle to traverse past simple adversarial content, where our strongest threats average an attack success rate (ASR) of 98.92%. We analyze trajectories to explore the failures of web agents and better understand vision-language model (VLM) limitations. WebGauntlet supports the study agent safety, demonstrating the gaps in performance between a spectrum of adversarial and safe environments.
true	INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving Theorem proving Synthetic benchmark dataset Generalization Transformers Graph neural networks Monte Carlo Tree Search In learning-assisted theorem proving, one of the most critical challenges is to generalize to theorems unlike those seen at training time. In this paper, we introduce INT, an INequality Theorem proving benchmark designed to test agents’ generalization ability. INT is based on a theorem generator, which provides theoretically infinite data and allows us to measure 6 different types of generalization, each reflecting a distinct challenge, characteristic of automated theorem proving. In addition, provides a fast theorem proving environment with sequence-based and graph-based interfaces, conducive to performing learning-based research. We introduce base-lines with architectures including transformers and graph neural networks (GNNs)for INT. Using INT, we find that transformer-based agents achieve stronger test performance for most of the generalization tasks, despite having much larger out-of-distribution generalization gaps than GNNs. We further find that the addition of Monte Carlo Tree Search (MCTS) at test time helps to prove new theorems.
true	Adversarial Learning of General Transformations for Data Augmentation GAN Data Augmentation Image Classification Data augmentation (DA) is fundamental against overfitting in large convolutional neural networks, especially with a limited training dataset. In images, DA is usually based on heuristic transformations, like geometric or color transformations. Instead of using predefined transformations, our work learns data augmentation directly from the training data by learning to transform images with an encoder-decoder architecture combined with a spatial transformer network. The transformed images still belong to the same class, but are new, more complex samples for the classifier. Our experiments show that our approach is better than previous generative data augmentation methods, and comparable to predefined transformation methods when training an image classifier.
true	SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness Algorithmic fairness invariance In this paper, we cast fair machine learning as invariant machine learning. We first formulate a version of individual fairness that enforces invariance on certain sensitive sets. We then design a transport-based regularizer that enforces this version of individual fairness and develop an algorithm to minimize the regularizer efficiently. Our theoretical results guarantee the proposed approach trains certifiably fair ML models. Finally, in the experimental studies we demonstrate improved fairness metrics in comparison to several recent fair training procedures on three ML tasks that are susceptible to algorithmic bias.
false	Offline Policy Optimization with Variance Regularization reinforcement learning offline batch RL off-policy policy optimization variance regularization Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications. This is often because off-policy RL algorithms suffer from distributional shift, due to mismatch between dataset and the target policy, leading to high variance and over-estimation of value functions. In this work, we propose variance regularization for offline RL algorithms, using stationary distribution corrections. We show that by using Fenchel duality, we can avoid double sampling issues for computing the gradient of the variance regularizer. The proposed algorithm for offline variance regularization can be used to augment any existing offline policy optimization algorithms. We show that the regularizer leads to a lower bound to the offline policy optimization objective, which can help avoid over-estimation errors, and explains the benefits of our approach across a range of continuous control domains when compared to existing algorithms.
true	Joining the Conversation: Towards Language Acquisition for Ad Hoc Team Play Emergent Communication Language Acquisition Ad Hoc Team Play In this paper, we propose and consider the problem of cooperative language acquisition as a particular form of the ad hoc team play problem. We then present a probabilistic model for inferring a speaker's intentions and a listener's semantics from observing communications between a team of language-users. This model builds on the assumptions that speakers are engaged in positive signalling and listeners are exhibiting positive listening, which is to say the messages convey hidden information from the listener, that then causes them to change their behaviour. Further, it accounts for potential sub-optimality in the speaker's ability to convey the right information (according to the given task). Finally, we discuss further work for testing and developing this framework.
true	Video Diffusion Models diffusion score video generative Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model for video generation that shows very promising initial results. Our model is a natural extension of the standard image diffusion architecture, and it enables jointly training from image and video data, which we find to reduce the variance of minibatch gradients and speed up optimization. To generate long and higher resolution videos we introduce a new conditional sampling technique for spatial and temporal video extension that performs better than previously proposed methods. We present the first results on a large text-conditioned video generation task, as well as state-of-the-art results on an established unconditional video generation benchmark. Supplementary material is available at https://video-diffusion.github.io.
false	Learning Online Data Association Data Association When an agent interacts with a complex environment, it receives a stream of percepts in which it may detect entities, such as objects or people. To build up a coherent, low-variance estimate of the underlying state, it is necessary to fuse information from multiple detections over time. To do this fusion, the agent must decide which detections to associate with one another. We address this data-association problem in the setting of an online filter, in which each observation is processed by aggregating into an existing object hypothesis. Classic methods with strong probabilistic foundations exist, but they are computationally expensive and require models that can be difficult to acquire. In this work, we use the deep-learning tools of sparse attention and representation learning to learn a machine that processes a stream of detections and outputs a set of hypotheses about objects in the world. We evaluate this approach on simple clustering problems, problems with dynamics, and a complex image-based domain. We find that it generalizes well from short to long observation sequences and from a few to many hypotheses, outperforming other learning approaches and classical non-learning methods.
true	DOF: Accelerating High-order Differential Operators with Forward Propagation PDE AI4Science Solving partial differential equations (PDEs) efficiently is essential for analyzing complex physical systems. Recent advancements in leveraging deep learning for solving PDE have shown significant promise. However, machine learning methods, such as Physics-Informed Neural Networks (PINN), face challenges in handling high-order derivatives of neural network-parameterized functions. Inspired by Forward Laplacian, a recent method on accelerating Laplacian computation, we propose an efficient computational framework, Differential Operator with Forward-propagation (DOF), for calculating general second-order differential operators without losing any precision. We provide rigorous proof of the advantages of our method over existing methods, demonstrating two times improvement in efficiency and reduced memory consumption on any architectures. Empirical results illustrate that our method surpasses traditional automatic differentiation (AutoDiff) techniques, achieving 2x improvement on the MLP structure and nearly 20x improvement on the MLP with Jacobian sparsity.
true	SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging Large Language Models Safety Safety Alignment Fine-Tuning Fine-tuning large language models (LLMs) on downstream tasks can inadvertently erode their safety alignment, even for benign fine-tuning datasets. We address this challenge by proposing SafeMERGE\footnote{Code available at: \url{https://github.com/aladinD/SafeMERGE}}, a post-fine-tuning framework that preserves safety while maintaining task utility. It achieves this by selectively merging fine-tuned and safety-aligned model layers \emph{only} when those deviate from safe behavior, measured by a cosine similarity criterion. We evaluate SafeMERGE against other fine-tuning- and post–fine-tuning-stage approaches for Llama-2-7B-Chat and Qwen-2-7B-Instruct models on GSM8K and PubMedQA tasks while exploring different merging strategies. We find that SafeMERGE consistently reduces harmful outputs compared to other baselines without significantly sacrificing performance, sometimes even enhancing it. The results suggest that our selective, subspace-guided, and per-layer merging method provides an effective safeguard against the inadvertent loss of safety in fine-tuned LLMs while outperforming simpler post–fine-tuning-stage defenses.
true	Physics-Informed Koopman Network for time-series prediction of dynamical systems Koopman operator physics-informed operator learning Nonlinear dynamical systems Koopman operator theory is receiving increased attention due to its promise to linearize nonlinear dynamics. Neural networks that are developed to represent Koopman operators have shown great success thanks to their ability to approximate arbitrarily complex functions. However, despite their great potential, they typically require large training data-sets either from measurements of a real system or from high-fidelity simulations. In this work, we propose a novel architecture inspired by physics-informed neural networks, which leverage automatic differentiation to impose the underlying physical laws via soft penalty constraints during model training. We demonstrate that it not only reduces the need of large training data-sets, but also maintains high effectiveness in approximating Koopman eigenfunctions.
false	Do KG-augmented Models Leverage Knowledge as Humans Do? neural symbolic reasoning knowledge graph interpretability model explanation faithfulness commonsense question answering recommender system Knowledge Graphs (KGs) can help neural-symbolic models to improve performance on various knowledge-intensive tasks, like recommendation systems and question answering. Concretely, neural reasoning over KGs may "explain" which information is relevant for inference. However, as an old saying goes, "seeing is not believing," it is natural to ask the question, "do KG-augmented models really behave as we expect？" This post presents the historical perspectives of KG-augmented models and discusses a recent work raising this question. Interestingly, empirical results demonstrate that perturbed KGs can maintain the downstream performance, which subvert our cognition over KG-augmented models' ability. We believe this topic is necessary and important for neural-symbolic reasoning and can guide future work on designing KG-augmented models.
true	Scalable Transfer Learning with Expert Models Transfer Learning Expert Models Few Shot Transfer of pre-trained representations can improve sample efficiency and reduce computational requirements for new tasks. However, representations used for transfer are usually generic, and are not tailored to a particular distribution of downstream tasks. We explore the use of expert representations for transfer with a simple, yet effective, strategy. We train a diverse set of experts by exploiting existing label structures, and use cheap-to-compute performance proxies to select the relevant expert for each target task. This strategy scales the process of transferring to new tasks, since it does not revisit the pre-training data during transfer. Accordingly, it requires little extra compute per target task, and results in a speed-up of 2-3 orders of magnitude compared to competing approaches. Further, we provide an adapter-based architecture able to compress many experts into a single model. We evaluate our approach on two different data sources and demonstrate that it outperforms baselines on over 20 diverse vision tasks in both cases.
true	In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness pre-training self-training theory robustness out-of-distribution unlabeled data auxiliary information multi-task learning theory distribution shift Consider a prediction setting with few in-distribution labeled examples and many unlabeled examples both in- and out-of-distribution (OOD). The goal is to learn a model which performs well both in-distribution and OOD. In these settings, auxiliary information is often cheaply available for every input. How should we best leverage this auxiliary information for the prediction task? Empirically across three image and time-series datasets, and theoretically in a multi-task linear regression setting, we show that (i) using auxiliary information as input features improves in-distribution error but can hurt OOD error; but (ii) using auxiliary information as outputs of auxiliary pre-training tasks improves OOD error. To get the best of both worlds, we introduce In-N-Out, which first trains a model with auxiliary inputs and uses it to pseudolabel all the in-distribution inputs, then pre-trains a model on OOD auxiliary outputs and fine-tunes this model with the pseudolabels (self-training). We show both theoretically and empirically that In-N-Out outperforms auxiliary inputs or outputs alone on both in-distribution and OOD error.
true	Adaptive Masked Weight Imprinting for Few-Shot Segmentation few-shot segmentation Deep learning has mainly thrived by training on large-scale datasets. However, for continual learning in applications such as robotics, it is critical to incrementally update its model in a sample efficient manner. We propose a novel method that constructs the new class weights from few labelled samples in the support set, while updating the previously learned classes. Inspiring from the work on adaptive correlation filters, an adaptive masked imprinted weights method is proposed. It utilizes a masked average pooling layer on the output embeddings and acts as a positive proxy for that class. It is then used to adaptively update the 1x1 convolutional filters that are responsible for the final classification. Our proposed method is evaluated on PASCAL-5i dataset and outperforms the state of the art in the 5-shot semantic segmentation. Unlike previous methods, our proposed approach does not require a second branch to estimate parameters or prototypes, and it enables the adaptation of previously learned weights. We further propose a novel setup for evaluating incremental object segmentation which we term as incremental PASCAL (iPASCAL), where our adaptation method has shown to outperform the baseline method.
true	Transient Non-stationarity and Generalisation in Deep Reinforcement Learning Reinforcement Learning Generalization Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments. For example, most RL algorithms collect new data throughout training, using a non-stationary behaviour policy. Due to the transience of this non-stationarity, it is often not explicitly addressed in deep RL and a single neural network is continually updated. However, we find evidence that neural networks exhibit a memory effect, where these transient non-stationarities can permanently impact the latent representation and adversely affect generalisation performance. Consequently, to improve generalisation of deep RL agents, we propose Iterated Relearning (ITER). ITER augments standard RL training by repeated knowledge transfer of the current policy into a freshly initialised network, which thereby experiences less non-stationarity during training. Experimentally, we show that ITER improves performance on the challenging generalisation benchmarks ProcGen and Multiroom.
false	Structure and randomness in planning and reinforcement learning reinforcement learning uncertainty model-based MCTS Planning in large state spaces inevitably needs to balance depth and breadth of the search. It has a crucial impact on planners performance and most manage this interplay implicitly. We present a novel method $\textit{Shoot Tree Search (STS)}$, which makes it possible to control this trade-off more explicitly. Our algorithm can be understood as an interpolation between two celebrated search mechanisms: MCTS and random shooting. It also lets the user control the bias-variance trade-off, akin to $TD(n)$, but in the tree search context. In experiments on challenging domains, we show that STS can get the best of both worlds consistently achieving higher scores.
false	A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning Multiagent reinforcement learning Meta-learning Non-stationarity A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other agents that are also simultaneously learning. In particular, each agent perceives the environment as effectively non-stationary due to the changing policies of other agents. Moreover, each agent is itself constantly learning, leading to natural nonstationarity in the distribution of experiences encountered. In this paper, we propose a novel meta-multiagent policy gradient theorem that directly accommodates for the non-stationary policy dynamics inherent to these multiagent settings. This is achieved by modeling our gradient updates to directly consider both an agent’s own non-stationary policy dynamics and the non-stationary policy dynamics of other agents interacting with it in the environment. We find that our theoretically grounded approach provides a general solution to the multiagent learning problem, which inherently combines key aspects of previous state of the art approaches on this topic. We test our method on several multiagent benchmarks and demonstrate a more efficient ability to adapt to new agents as they learn than previous related approaches across the spectrum of mixed incentive, competitive, and cooperative environments.
false	On Linear Identifiability of Learned Representations identifiability analysis deep learning representation learning probabilistic discriminative models Identifiability is a desirable property of a statistical model: it implies that the true model parameters may be estimated to any desired precision, given sufficient computational resources and data. We study identifiability in the context of representation learning: discovering nonlinear data representations that are optimal with respect to some downstream task. When parameterized as deep neural networks, such representation functions lack identifiability in parameter space, because they are overparameterized by design. In this paper, building on recent advances in nonlinear Independent Components Analysis, we aim to rehabilitate identifiability by showing that a large family of discriminative models are in fact identifiable in function space, up to a linear indeterminacy. Many models for representation learning in a wide variety of domains have been identifiable in this sense, including text, images and audio, state-of-the-art at time of publication. We derive sufficient conditions for linear identifiability and provide empirical support for the result on both simulated and real-world data.
true	Learning with Feature-Dependent Label Noise: A Progressive Approach Noisy Label Deep Learning Classification Label noise is frequently observed in real-world large-scale datasets. The noise is introduced due to a variety of reasons; it is heterogeneous and feature-dependent. Most existing approaches to handling noisy labels fall into two categories: they either assume an ideal feature-independent noise, or remain heuristic without theoretical guarantees. In this paper, we propose to target a new family of feature-dependent label noise, which is much more general than commonly used i.i.d. label noise and encompasses a broad spectrum of noise patterns. Focusing on this general noise family, we propose a progressive label correction algorithm that iteratively corrects labels and refines the model. We provide theoretical guarantees showing that for a wide variety of (unknown) noise patterns, a classifier trained with this strategy converges to be consistent with the Bayes classifier. In experiments, our method outperforms SOTA baselines and is robust to various noise types and levels.
true	Object-centric Compositional Imagination for Visual Abstract Reasoning object-centric visual reasoning imagination compositional generalization Like humans devoid of imagination, current machine learning systems lack the ability to adapt to new, unexpected situations by foreseeing them, which makes them unable to solve new tasks by analogical reasoning. In this work, we introduce a new compositional imagination framework that improves a model's ability to generalize. One of the key components of our framework is object-centric inductive biases that enables models to perceive the environment as a series of objects, properties, and transformations. By composing these key ingredients, it is possible to generate new unseen tasks that, when used to train the model, improve generalization. Experiments on a simplified version of the Abstraction and Reasoning Corpus (ARC) demonstrate the effectiveness of our framework.
false	Searching for Convolutions and a More Ambitious NAS neural architecture search automated machine learning convolutional neural networks An important goal of neural architecture search (NAS) is to automate-away the design of neural networks on new tasks in under-explored domains, thus helping to democratize machine learning. However, current NAS research largely focuses on search spaces consisting of existing operations---such as different types of convolution---that are already known to work well on well-studied problems---often in computer vision. Our work is motivated by the following question: can we enable users to build their own search spaces and discover the right neural operations given data from their specific domain? We make progress towards this broader vision for NAS by introducing a space of operations generalizing the convolution that enables search over a large family of parameterizable linear-time matrix-vector functions. Our flexible construction allows users to design their own search spaces adapted to the nature and shape of their data, to warm-start search methods using convolutions when they are known to perform well, or to discover new operations from scratch when they do not. We evaluate our approach on several novel search spaces over vision and text data, on all of which simple NAS search algorithms can find operations that perform better than baseline layers.
true	Dynamic Knowledge Integration in Multi-Agent Systems for Content Inference Multi agent LLM Knowledge integration Knowledge representation and reasoning Advancements in cutting-edge science and technology have resulted from the integration of multiple interdisciplinary domains beyond traditional academic boundaries. Achieving effective cross-domain knowledge-sharing and consensus-building is crucial. However, single-agent Large Language Models (LLMs) solutions often struggle to integrate the diverse and highly specialized knowledge required in these contexts. This study proposes a multi-agent system with dynamic knowledge integration, where multiple specialized LLM-based agents cooperatively infer content by referencing different domain-specific databases. Each agent selectively and dynamically updates references based on conversational context to achieve deeper insight and more robust solutions. We propose four system architectures---Decentralized, Centralized, Layered, and Shared Pool---for agent coordination. We then evaluate these approaches on a title-to-abstract inference task using a subset of the arXiv dataset, demonstrating that multi-agent systems significantly outperform single-agent models in both accuracy and stability. Notably, expert agents, restricted to domain-specific data, produce more precise and consistent outputs, and the Decentralized architecture fosters increased domain interaction. These findings suggest that the collaboration of specialized multi-agent systems can more effectively facilitate the consensus-building process in the advancement of complex interdisciplinary scientific domains.
false	On the Importance of Distraction-Robust Representations for Robot Learning Unsupervised Representation Learning Robot Control Quality-Diversity Representation Learning methods can allow the application of Reinforcement Learning algorithms when a high dimensionality in a robot's perceptions would otherwise prove prohibitive. Consequently, unsupervised Representation Learning components often feature in robot control algorithms that assume high-dimensional camera images as the principal source of information. In their design and performance, these algorithms often benefit from the controlled nature of the simulation or laboratory conditions they are evaluated in. However, these settings fail to acknowledge the stochasticity of most real-world environments. In this work, we introduce the concept of Distraction-Robust Representation Learning. We argue that environment noise and other distractions require learned representations to encode the robot's expected perceptions rather than the observed ones. Our experimental evaluations demonstrate that representations learned with a traditional dimensionality reduction algorithm are strongly susceptible to distractions in a robot's environment. We propose an Encoder-Decoder architecture that produces representations that allow the learning outcomes of robot control tasks to remain unaffected by these distractions.
false	LATENT OPTIMIZATION VARIATIONAL AUTOENCODER FOR CONDITIONAL MOLECULAR GENERATION latent optimization Variational Autoencoder molecular generation Variational autoencoder (VAE) is a generation algorithm, consisting of an encoder and a decoder, and the latent variable from the encoder is used as the input of the decoder. VAE is widely used for image, audio and text generation tasks. In general, the training of VAE is at risk of posterior collapsing especially for long sequential data. To alleviate this, modified evidence lower bounds (ELBOs) were proposed. However, these approaches heuristically control training loss using a hyper-parameter, and it is not way to solve the fundamental problem of vanilla VAE. In this paper, we propose a method to insert an optimization step of the latent variable and alternately update the encoder and decoder of conditional VAE for maximizing ELBOs. In experiments, we applied the latent optimization VAE (LOVAE) on ZINC database, consisting of string representation of molecules, for the inverse molecular design. We showed that the proposed LOVAE achieves better performance than vanilla VAE in terms of ELBOs and molecular generation performance. In addition, the proposed method showed better performance in property satisfaction and property maximization tasks compared to existing works.
false	MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning mqes entropy search epistemic efficient exploration continuous reinforcement exploration policy aleatoric uncertainty principle optimism The principle of optimism in the face of (aleatoric and epistemic) uncertainty has been utilized to design efficient exploration strategies for Reinforcement Learning (RL). Different from most prior work targeting at discrete action space, we propose a generally information-theoretic exploration principle called Max-Q Entropy Search (MQES) for continuous RL algorithms. MQES formulates the exploration policy to maximize the information about the globally optimal distribution of $Q$ function, which could explore optimistically and avoid over-exploration by recognizing the epistemic and aleatoric uncertainty, respectively. To make MQES practically tractable, we firstly incorporate distributional and ensemble $Q$ function approximations to MQES, which could formulate the epistemic and aleatoric uncertainty accordingly. Then, we introduce a constraint to stabilize the training and solve the constrained MQES problem to derive the exploration policy in closed form. Empirical evaluations show that MQES outperforms state-of-the-art algorithms on Mujoco environments.
false	Consistency and Monotonicity Regularization for Neural Knowledge Tracing knowledge tracing data augmentation regularization Knowledge Tracing (KT), tracking a human's knowledge acquisition, is a central component in online learning and AI in Education. In this paper, we present a simple, yet effective strategy to improve the generalization ability of KT models: we propose three types of novel data augmentation, coined replacement, insertion, and deletion, along with corresponding regularization losses that impose certain consistency or monotonicity bias on model's predictions for the original and augmented sequence. Extensive experiments on various KT benchmarks show that our regularization scheme significantly improve the prediction performances, under 3 widely-used neural networks and 4 public benchmarks for KT, e.g., it yields 6.3% improvement in AUC under the DKT model and the ASSISTmentsChall dataset.
true	Learning to Defense by Learning to Attack Adversarial Training Learning to Learn/Optimize Nonconvex-Nonconcave Minmax Optimization Adversarial training provides a principled approach for training robust neural networks. From an optimization perspective, the adversarial training is essentially solving a minmax robust optimization problem. The outer minimization is trying to learn a robust classifier, while the inner maximization is trying to generate adversarial samples. Unfortunately, such a minmax problem is very difficult to solve due to the lack of convex-concave structure. This work proposes a new adversarial training method based on a general learning-to-learn framework. Specifically, instead of applying the existing hand-design algorithms for the inner problem, we learn an optimizer, which is parametrized as a convolutional neural network. At the same time, a robust classifier is learned to defense the adversarial attack generated by the learned optimizer. From the perspective of generative learning, our proposed method can be viewed as learning a deep generative model for generating adversarial samples, which is adaptive to the robust classification. Our experiments demonstrate that our proposed method significantly outperforms existing adversarial training methods on CIFAR-10 and CIFAR-100 datasets.
false	Non-iterative Parallel Text Generation via Glancing Transformer models transformer parallel text generation multiple decoding iterations generation remarkable inference autoregressive counterparts prediction accuracy best accuracy inference speed Although non-autoregressive models with one-iteration generation achieve remarkable inference speed-up, they still fall behind their autoregressive counterparts in prediction accuracy. The non-autoregressive models with the best accuracy currently rely on multiple decoding iterations, which largely sacrifice the inference speed of non-autoregressive models. Inspired by the way of learning word dependencies in autoregressive and iterative-decoding models, we propose Glancing Transformer (GLAT) with a glancing language model (GLM), which learns to capture the word dependency gradually. Experiments on three benchmarks demonstrate that our approach can significantly improve the accuracy of non-autoregressive models without multiple decoding iterations. In particular, GLAT achieves state-of-the-art results among non-iterative models and even outperforms top iterative counterparts in some specific benchmarks.
false	CorDial: Coarse-to-fine Abstractive Dialogue Summarization with Controllable Granularity dialogue summarization controllable generation natural language processing Dialogue summarization is challenging due to its multi-speaker standpoints, casual spoken language, and limited labeled data. In this paper, we propose CorDial, aiming to improve the abstractive dialogue summarization quality and at the same time enable granularity controllability. We propose 1) a coarse-to-fine generation strategy that generates a summary draft followed by a final summary in an autoregressive way. The summary draft, which provides weakly-supervised signals, is composed of pseudo-labeled interrogative pronoun categories and noisy key phrases extracted with a constituency parser. 2) A simple strategy to control the granularity of the final summary. CorDial can predict and control the number of summary sentences for a given dialogue by predicting and highlighting different text spans from the source text. Our model achieves state-of-the-art performance on the largest dialogue summarization corpus SAMSum. We conduct comprehensive error analysis and show competitive human evaluation results to annotated summaries.
true	CaPC Learning: Confidential and Private Collaborative Learning machine learning deep learning privacy confidentiality security homomorphic encryption mpc differential privacy Machine learning benefits from large training datasets, which may not always be possible to collect by any single entity, especially when using privacy-sensitive data. In many contexts, such as healthcare and finance, separate parties may wish to collaborate and learn from each other's data but are prevented from doing so due to privacy regulations. Some regulations prevent explicit sharing of data between parties by joining datasets in a central location (confidentiality). Others also limit implicit sharing of data, e.g., through model predictions (privacy). There is currently no method that enables machine learning in such a setting, where both confidentiality and privacy need to be preserved, to prevent both explicit and implicit sharing of data. Federated learning only provides confidentiality, not privacy, since gradients shared still contain private information. Differentially private learning assumes unreasonably large datasets. Furthermore, both of these learning paradigms produce a central model whose architecture was previously agreed upon by all parties rather than enabling collaborative learning where each party learns and improves their own local model. We introduce Confidential and Private Collaborative (CaPC) learning, the first method provably achieving both confidentiality and privacy in a collaborative setting. We leverage secure multi-party computation (MPC), homomorphic encryption (HE), and other techniques in combination with privately aggregated teacher models. We demonstrate how CaPC allows participants to collaborate without having to explicitly join their training sets or train a central model. Each party is able to improve the accuracy and fairness of their model, even in settings where each party has a model that performs well on their own dataset or when datasets are not IID and model architectures are heterogeneous across parties.
true	Semiparametric Inference and Equation Discovery with the Bayesian Machine Scientist symbolic regression Bayesian inference hybrid modeling Earth and climate sciences Hybrid modeling, combining machine learning with physical equations, is promising in many fields of science, in particular for climate and Earth Sciences, but faces challenges like interpretability, inconsistent extrapolation, lack of speed, and robust inference. Here we show that the Bayesian machine scientist, a Bayesian approach to symbolic regression is an ideal choice for the challenges in the hybrid modeling task. We formulate the hybrid Bayesian machine scientist and showcase its potential in the example of modeling ecosystem respiration with the $Q_{10}$ model. Specifically, we show that our proposed hybrid equation discovery method (i) extracts the correct equations, (ii) extrapolates better in different scenarios than the non-hybrid and deep-learning-based baselines, and (iii) is able to infer more accurately parameters of interest, even in the presence of equifinality. We anticipate a spur of development of hybrid equation discovery algorithms in the sciences to approach fully interpretable data-driven models.
false	Neural Ensemble Search for Uncertainty Estimation and Dataset Shift uncertainty estimation deep ensemble dataset shift robustness uncertainty calibration Ensembles of neural networks achieve superior performance compared to stand-alone networks not only in terms of predictive performance, but also uncertainty calibration and robustness to dataset shift. Diversity among networks is believed to be key for building strong ensembles, but typical approaches, such as \emph{deep ensembles}, only ensemble different weight vectors of a fixed architecture. Instead, we propose two methods for constructing ensembles to exploit diversity among networks with \emph{varying} architectures. We find that the resulting ensembles are indeed more diverse and also exhibit better uncertainty calibration, predictive performance and robustness to dataset shift in comparison with deep ensembles on a variety of classification tasks.
false	Image Modeling with Deep Convolutional Gaussian Mixture Models Gaussian Mixture Model Deep Learning Unsupervised Representation Learning Sampling In this conceptual work, we present DCGMM, a deep hierarchical Gaussian Mixture Model (GMM) that is particularly suited for describing and generating images. Vanilla (i.e., "flat") GMMs require a very large number of components to well describe images, leading to long training times and memory issues. DCGMMs avoid this by a stacked architecture of multiple GMM layers, linked by convolution and pooling operations. This allows to exploit the compositionality of images in a similar way as deep CNNs do. This sets them apart from vanilla GMMs which are trained by EM, requiring a prior k-means initialization which is infeasible in a layered structure. For generating sharp images with DCGMM, we introduce a new gradient-based technique for sampling through non-invertible operations like convolution and pooling. Based on the MNIST and FashionMNIST datasets, we validate the DCGMM model by demonstrating its superiority over "flat" GMMs for clustering, sampling and outlier detection. We additionally demonstrate the applicability of DCGMM to variant generation, in-painting and class-conditional sampling.
true	Unifying semi-supervised and robust learning by mixup label noise semi-supervised learning robust leaning under to noisy label Supervised deep learning methods require cleanly labeled large-scale datasets, but collecting such data is difficult and sometimes impossible. There exist two popular frameworks to alleviate this problem: semi-supervised learning and robust learning to label noise. Although these frameworks relax the restriction of supervised learning, they are studied independently. Hence, the training scheme that is suitable when only small cleanly-labeled data are available remains unknown. In this study, we consider learning from bi-quality data as a generalization of these studies, in which a small portion of data is cleanly labeled, and the rest is corrupt. Under this framework, we compare recent algorithms for semi-supervised and robust learning. The results suggest that semi-supervised learning outperforms robust learning with noisy labels. We also propose a training strategy for mixing mixup techniques to learn from such bi-quality data effectively.
false	Deep Coherent Exploration For Continuous Control reinforcement learning exploration latent variable models In policy search methods for reinforcement learning (RL), exploration is often performed by injecting noise either in action space at each step independently or in parameter space over each full trajectory. In prior work, it has been shown that with linear policies, a more balanced trade-off between these two exploration strategies is beneficial. However, that method did not scale to policies using deep neural networks. In this paper, we introduce Deep Coherent Exploration, a general and scalable exploration framework for deep RL algorithms on continuous control, that generalizes step-based and trajectory-based exploration. This framework models the last layer parameters of the policy network as latent variables and uses a recursive inference step within the policy update to handle these latent variables in a scalable manner. We find that Deep Coherent Exploration improves the speed and stability of learning of A2C, PPO, and SAC on several continuous control tasks.
false	Improving Sequence Generative Adversarial Networks with Feature Statistics Alignment feature statistics alignment signals sequence generation gan great challenges sequences discrete elements mode dropping Generative Adversarial Networks (GAN) are facing great challenges in synthesizing sequences of discrete elements, such as mode dropping and unstable training. The binary classifier in the discriminator may limit the capacity of learning signals and thus hinder the advance of adversarial training. To address such issues, apart from the binary classification feedback, we harness a Feature Statistics Alignment (FSA) paradigm to deliver fine-grained signals in the latent high-dimensional representation space. Specifically, FSA forces the mean statistics of the fake data distribution to approach that of real data as close as possible in a finite-dimensional feature space. Experiments on synthetic and real benchmark datasets show the superior performance in quantitative evaluation and demonstrate the effectiveness of our approach to discrete sequence generation. To the best of our knowledge, the proposed architecture is the first that employs feature alignment regularization in the Gumbel-Softmax based GAN framework for sequence generation.
false	Hybrid Discriminative-Generative Training via Contrastive Learning Hybrid Models Contrastive Learning Energy-Based Models Discriminative-Generative Models Contrastive learning and supervised learning have both seen significant progress and success. However, thus far they have largely been treated as two separate objectives, brought together only by having a shared neural network. In this paper we show that through the perspective of hybrid discriminative-generative training of energy-based models we can make a direct connection between contrastive learning and supervised learning. Beyond presenting this unified view, we show our specific choice of approximation of the energy-based loss significantly improves energy-based models and contrastive learning based methods in confidence-calibration, out-of-distribution detection, adversarial robustness, generative modeling, and image classification tasks. In addition to significantly improved performance, our method also gets rid of SGLD training and does not suffer from training instability. Our evaluations also demonstrate that our method performs better than or on par with state-of-the-art hand-tailored methods in each task.
true	MALIBU Benchmark: Multi-Agent LLM Implicit Bias Uncovered LLM Implicit Bias Multi-agent AI Alignment Persona Multi-agent systems, which consist of multiple AI models interacting within a shared environment, are increasingly used for persona-based interactions. However, if not carefully designed, these systems can reinforce implicit biases in large language models (LLMs), raising concerns about fairness and equitable representation. We present MALIBU\footnote{You can find the MALIBU Benchmark here: \url{https://anonymous.4open.science/r/MALIBU-Benchmark-228C}}, a novel benchmark developed to assess the degree to which LLM-based multi-agent systems implicitly reinforce social biases and stereotypes. MALIBU evaluates bias in LLM-based multi-agent systems through scenario-based assessments. AI models complete tasks within predefined contexts, and their responses undergo evaluation by an LLM-based multi-agent judging system in two phases. In the first phase, judges score responses labeled with specific demographic personas (e.g., gender, race, religion) across four metrics. In the second phase, judges compare paired responses assigned to different personas, scoring them and selecting the superior response. Our study quantifies biases in LLM-generated outputs, revealing that bias mitigation may favor marginalized personas over true neutrality, emphasizing the need for nuanced detection, balanced fairness strategies, and transparent evaluation benchmarks in multi-agent systems.
true	DAG Learning on the Permutahedron structure learning directed acyclic graphs We introduce Daguerro, a strategy for learning directed acyclic graphs (DAGs). In contrast to previous methods, our problem formulation (i) guarantees to learn a DAG, (ii) does not propagate errors over multiple stages, and (iii) can be trained end-to-end without pre-processing steps. Our algorithm leverages advances in differentiable sparse structured inference for learning a total ordering of the variables in the simplex of permutation vectors (the permutahedron), allowing for a substantial reduction in memory and time complexities w.r.t. existing permutation-based continuous optimization methods.
true	Control-Aware Representations for Model-based Reinforcement Learning representations control representation learning carl reinforcement latent space major challenge modern reinforcement learning efficient control A major challenge in modern reinforcement learning (RL) is efficient control of dynamical systems from high-dimensional sensory observations. Learning controllable embedding (LCE) is a promising approach that addresses this challenge by embedding the observations into a lower-dimensional latent space, estimating the latent dynamics, and utilizing it to perform control in the latent space. Two important questions in this area are how to learn a representation that is amenable to the control problem at hand, and how to achieve an end-to-end framework for representation learning and control. In this paper, we take a few steps towards addressing these questions. We first formulate a LCE model to learn representations that are suitable to be used by a policy iteration style algorithm in the latent space.We call this model control-aware representation learning(CARL). We derive a loss function and three implementations for CARL. In the offline implementation, we replace the locally-linear control algorithm (e.g., iLQR) used by the existing LCE methods with a RL algorithm, namely model-based soft actor-critic, and show that it results in significant improvement. In online CARL, we interleave representation learning and control, and demonstrate further gain in performance. Finally, we propose value-guided CARL, a variation in which we optimize a weighted version of the CARL loss function, where the weights depend on the TD-error of the current policy. We evaluate the proposed algorithms by extensive experiments on benchmark tasks and compare them with several LCE baselines.
false	HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs Hypergraph Representation Learning Inductive Learning Geometric Deep Learning Aggregation Methods Graphs are the most ubiquitous form of structured data representation used in machine learning. They model, however, only pairwise relations between nodes and are not designed for encoding the higher-order relations found in many real-world datasets. To model such complex relations, hypergraphs have proven to be a natural representation. Learning the node representations in a hypergraph is more complex than in a graph as it involves information propagation at two levels: within every hyperedge and across the hyperedges. Most current approaches first transform a hypergraph structure to a graph for use in existing geometric deep learning algorithms. This transformation leads to information loss, and sub-optimal exploitation of the hypergraph's expressive power. We present HyperSAGE, a novel hypergraph learning framework that uses a two-level neural message passing strategy to accurately and efficiently propagate information through hypergraphs. The flexible design of HyperSAGE facilitates different ways of aggregating neighborhood information. Unlike the majority of related work which is transductive, our approach, inspired by the popular GraphSAGE method, is inductive. Thus, it can also be used on previously unseen nodes, facilitating deployment in problems such as evolving or partially observed hypergraphs. Through extensive experimentation, we show that HyperSAGE outperforms state-of-the-art hypergraph learning methods on representative benchmark datasets. We also demonstrate that the higher expressive power of HyperSAGE makes it more stable in learning node representations as compared to the alternatives.
false	iPTR: Learning a representation for interactive program translation retrieval iptr representation big code codebases obsolete language modern Program translation contributes to many real world scenarios, such as porting codebases written in an obsolete or deprecated language to a modern one or re-implementing existing projects in one's preferred programming language. Existing data-driven approaches either require large amounts of training data or neglect significant characteristics of programs. In this paper, we present iPTR for interactive code translation retrieval from Big Code. iPTR uses a novel code representation technique that encodes structural characteristics of a program and a predictive transformation technique to transform the representation into the target programming language. The transformed representation is used for code retrieval from Big Code. With our succinct representation, the user can easily update and correct the returned results to improve the retrieval process. Our experiments show that iPTR outperforms supervised baselines in terms of program accuracy.
true	Compositional Multi-object Reinforcement Learning with Linear Relation Networks reinforcement settings number compositional linear relation networks reinforcement learning remarkable progress last years robust dexterous Although reinforcement learning has seen remarkable progress over the last years, solving robust dexterous object-manipulation tasks in multi-object settings remains a challenge. In this paper, we focus on models that can learn manipulation tasks in fixed multi-object settings \emph{and} extrapolate this skill zero-shot without any drop in performance when the number of objects changes. We consider the generic task of bringing a specific cube out of a set to a goal position. We find that previous approaches, which primarily leverage attention and graph neural network-based architectures, do not generalize their skills when the number of input objects changes while scaling as $K^2$. We propose an alternative plug-and-play module based on relational inductive biases to overcome these limitations. Besides exceeding performances in their training environment, we show that our approach, which scales linearly in $K$, allows agents to extrapolate and generalize zero-shot to any new object number.
false	Analysis of Alignment Phenomenon in Simple Teacher-student Networks with Finite Width alignment finite width network teacher student model angular distance function Recent theoretical analysis suggests that ultra-wide neural networks always converge to global minima near the initialization under first order methods. However, the convergence property of neural networks with finite width could be very different. The simplest experiment with two-layer teacher-student networks shows that the input weights of student neurons eventually align with one of the teacher neurons. This suggests a distinct convergence nature for ``not-too-wide'' neural networks that there might not be any local minima near the initialization. As the theoretical justification, we prove that under the most basic settings, all student neurons must align with the teacher neuron at any local minima. The methodology is extendable to more general cases, where the proof can be reduced to analyzing the properties of a special class of functions that we call {\em Angular Distance (AD) function}. Finally, we demonstrate that these properties can be easily verified numerically.
true	Solving Poisson Equations using Neural Walk-on-Spheres Monte Carlo methods partial differential equations deep learning We propose Neural Walk-on-Spheres (NWoS), a novel neural PDE solver for the efficient solution of high-dimensional Poisson equations. Leveraging stochastic representations and Walk-on-Spheres methods, we develop novel losses for neural networks based on the recursive solution of Poisson equations on spheres inside the domain. The resulting method is highly parallelizable and does not require spatial gradients for the loss. We provide a comprehensive comparison against competing methods based on PINNs, the Deep Ritz method, (backward) stochastic differential equations, and neural cache. In several challenging, high-dimensional numerical examples, we demonstrate the superiority of NWoS in terms of accuracy, speed, and computational costs. Compared to commonly used PINNs, our approach can reduce memory usage and errors by orders of magnitude. Furthermore, we apply NWoS to problems in the context of PDE-constrained optimization as well as molecular dynamics to show its efficiency in practical applications
false	Simple deductive reasoning tests and numerical data sets for exposing limitation of today's deep neural networks inductive reasoning deductive reasoning neural network memory feature engineering Learning for Deductive Reasoning is an open problem in the machine learning world today. Deductive reasoning involves storing facts in memory and generation of newer facts over time. The concept of memory, processor and code in deduction systems is fundamentally different from the purpose and formulation of weights in a deep neural network. A majority of the machine learning models are inductive reasoning models including state of the art deep neural networks which are effectively tensor interpolation based models. A step towards realization of memory is through recurrent neural networks and its variants, however the formal representation is not sufficient enough to capture a complex mapping function between input and output patterns. Deep neural networks are positioned to do away with feature engineering which is essentially deductive reasoning methodology. There are existing works in deductive reasoning in neural networks that require learning of syntax, unification and deduction and operate on text data as sequence of tokens. However the performance of deductive reasoning networks is far from perfection which may be either due to syntax or deduction aspects. In this context, we have proposed a suite of completely numeric data sets which do not require parsing as with text data. The 10 data sets are for - (a) selection (3 data sets) - minimum, maximum and top 2nd element in an array of numbers; (b) matching (3 data sets) - duplicate detection, counting and histogram learning; (c) divisibility tests (2 data sets) - divisibility of two numbers and divisibility by 3; (d) representation (2 data sets) - binary representation and parity. Though extremely simple in terms of feature engineering, in all of these tests, simple deep neural networks, random forest and recurrent neural networks have failed with very low accuracies. We propose these as numerical test-bed for testing learning models for deductive reasoning.
false	Prototypical Representation Learning for Low-resource Knowledge Extraction: Summary and Perspective Prototype Low-resource Knowledge Extraction Recent years have witnessed the success of prototypical representation in widespread low-resource tasks, since "Prototypical Networks for Few-shot Learning (NeurIPS 2017)" proposed to represent each class as a prototype by the mean of its instance embeddings and learn a metric space in which classification can be performed by computing distances to prototypes. A recent paper "Prototypical Representation Learning for Relation Extraction" accepted by ICLR 2021, as a member of the growing zoo of prototypical networks, has addressed prototypical representation learning for low-resource knowledge extraction. In this post, we briefly summarize this issue by highlighting the ICLR paper. Different from vanilla prototypical networks, this ICLR paper has proposed to tackle low-resource knowledge extraction (1) considering both compactness intra each prototype and separability inter prototypes, (2) by leveraging contrastive learning and projecting prototypes into geometric space. Furthermore, we also point out some shortcomings of this paper and put forward some promising directions.
true	Disentangling Factors of Variations Using Few Labels labels disentangled representations factors variations access model selection training promising research direction representation learning locatello Learning disentangled representations is considered a promising research direction in representation learning. Recently, Locatello et al. (2018) demonstrated that the unsupervised learning of disentangled representations is theoretically impossible and that state-of-the-art methods, which are often unsupervised, require access to annotated examples to select good model runs. Yet, if we assume access to labels for model selection, it is not clear why we should not use them directly for training. In this paper, we first show that model selection using few labels is feasible. Then, as a proof-of-concept, we consider a simple semi-supervised method that directly uses the labels for training. We train more than 7000 models and empirically validate that collecting a handful of potentially noisy labels is sufficient to learn disentangled representations.
true	Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answering Large Language Model Hallucination Faithful Reasoning Knowledge Graph-based Question Answering Recent works integrating Knowledge Graphs (KGs) have led to promising improvements in enhancing the reasoning accuracy of Large Language Models (LLMs). However, current benchmarks focus mainly on closed-ended tasks, leaving a gap in the assessment of more complex real-world scenarios. This gap has also obscured the evaluation of KGs' potential to mitigate the problem of hallucination in LLMs. To fill the gap, we introduce OKGQA, a new benchmark specifically designed to assess LLMs enhanced with KGs under open-ended, real-world question answering scenarios. OKGQA is designed to closely reflect the complexities of practical applications using questions from different types, and incorporates specific metrics to measure both hallucination ratio and the enhancement in reasoning capabilities. To consider the scenario in which KGs may have varying levels of mistakes, we propose another benchmark variant OKGQA-P to assess model performance when the semantics and structure of KGs are deliberately perturbed and contaminated. OKGQA aims to (1) explore whether KGs can make LLMs more trustworthy in an open-ended setting, and (2) conduct a comparative analysis to shed light on method design. We believe that this study can facilitate a more complete performance comparison and encourage continuous improvement in integrating KGs with LLMs to reduce hallucination.
true	Situated Communication: A Solution to Over-communication between Artificial Agents Emergent communication Multi-agent communication Multi-step interactions Artificial agents Most research on communication emergence between reinforcement learning (RL) agents explores unsituated communication in one-step referential tasks. The tasks are not temporally interactive and lack time pressures typically present in natural communication and language learning. In these settings, agents can successfully learn to communicate, but they do not learn to exchange information concisely—they tend towards over-communication and an anti-efficient encoding. In our work, we introduce situated communication by imposing an opportunity cost on communication—the acting agent has to forgo an action to solicit information from its advisor. Situated communication mimics the external pressure of passing time in real-world communication. We compare language emergence under this pressure against language learning with an internal cost on articulation, implemented as a per-message penalty. We find that while both pressures can disincentivise over-communication, situated communication does it more effectively and, unlike the internal pressure, does not negatively impact communication emergence. Implementing an opportunity cost on communication might be key to shaping language properties and incentivising concise information sharing between artificial agents.
false	META-LEARNING FOR SCIENTIFIC HYPOTHESIS GENERATION AND EXPERIMENTAL DESIGN Meta-Learning Reinforcement Learning Scientific Discovery Few-Shot Learning Hypothesis Generation Experimental Design Agentic AI Multi-Domain Adaptation Bayesian Optimization Automated Experimentation Generating novel scientific hypotheses and designing experiments often requires deep domain expertise and substantial time investment. This paper proposes a meta-learning framework to accelerate hypothesis generation and experimental design using agentic AI systems. The approach trains AI agents across diverse scientific domains (e.g., materials science, drug discovery, physics simulations), enabling rapid adaptation to new research problems with minimal labeled data. Specifically, a few-shot learning mechanism facilitates domain transfer, while a reinforcement learning (RL) engine autonomously refines experimental parameters under resource constraints. Experimental results demonstrate a 40% reduction in design iterations and 25% faster convergence on valid hypotheses, statistically validated with p < 0.05. These findings highlight the potential of meta-learning and RL to expedite scientific discovery, reduce trial-and-error, and improve research efficiency. Future work will explore formal theoretical guarantees, benchmarking against SOTA approaches, and real-world validation in laboratory settings.
true	Conformal Structured Prediction Conformal Prediction Structured Prediction Integer Programming Conformal prediction has recently emerged as a promising strategy for quantifying the uncertainty of a predictive model; these algorithms modify the model to output sets of labels that are guaranteed to contain the true label with high probability. However, existing conformal prediction algorithms have largely targeted classification and regression settings, where the structure of the prediction set has a simple form as a level set of the scoring function. However, for complex structured outputs such as text generation, these prediction sets might include a large number of labels and therefore be hard for users to interpret. In this paper, we propose a general framework for conformal prediction in the structured prediction setting, that modifies existing conformal prediction algorithms to output structured prediction sets that implicitly represent sets of labels. In addition, we demonstrate how our approach can be applied in domains where the prediction sets can be represented as a set of nodes in a directed acyclic graph; for instance, for hierarchical labels such as image classification, a prediction set might be a small subset of coarse labels implicitly representing the prediction set of all their more fine-descendants. We demonstrate how our algorithm can be used to construct prediction sets that satisfy a desired coverage guarantee in several domains.
false	Approximation Algorithms for Sparse Principal Component Analysis Sparse PCA Principal component analysis Randomized linear algebra Singular value decomposition Principal component analysis (PCA) is a widely used dimension reduction technique in machine learning and multivariate statistics. To improve the interpretability of PCA, various approaches to obtain sparse principal direction loadings have been proposed, which are termed Sparse Principal Component Analysis (SPCA). In this paper, we present three provably accurate, polynomial time, approximation algorithms for the SPCA problem, without imposing any restrictive assumptions on the input covariance matrix. The first algorithm is based on randomized matrix multiplication; the second algorithm is based on a novel deterministic thresholding scheme; and the third algorithm is based on a semidefinite programming relaxation of SPCA. All algorithms come with provable guarantees and run in low-degree polynomial time. Our empirical evaluations confirm our theoretical findings.
false	A spherical analysis of Adam with Batch Normalization Deep Learning Machine Learning Adam Batch Normalization Batch Normalization (BN) is a prominent deep learning technique. In spite of its apparent simplicity, its implications over optimization are yet to be fully understood. While previous studies mostly focus on the interaction between BN and stochastic gradient descent (SGD), we develop a geometric perspective which allows us to precisely characterize the relation between BN and Adam. More precisely, we leverage the radial invariance of groups of parameters, such as filters for convolutional neural networks, to translate the optimization steps on the $L_2$ unit hypersphere. This formulation and the associated geometric interpretation shed new light on the training dynamics. Firstly, we use it to derive the first effective learning rate expression of Adam. Then we show that, in the presence of BN layers, performing SGD alone is actually equivalent to a variant of Adam constrained to the unit hypersphere. Finally, our analysis outlines phenomena that previous variants of Adam act on and we experimentally validate their importance in the optimization process.
true	Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows Conditional Sampling Normalizing Flows Markov Chain Monte Carlo Missing Data Inference We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a technique for sampling from the exact conditional distributions learned by normalizing flows. As a conditional sampling method, PL-MCMC enables Monte Carlo Expectation Maximization (MC-EM) training of normalizing flows from incomplete data. Through experimental tests applying normalizing flows to missing data tasks for a variety of data sets, we demonstrate the efficacy of PL-MCMC for conditional sampling from normalizing flows.
true	Variational Intrinsic Control Revisited Unsupervised reinforcement learning Information theory In this paper, we revisit variational intrinsic control (VIC), an unsupervised reinforcement learning method for finding the largest set of intrinsic options available to an agent. In the original work by Gregor et al. (2016), two VIC algorithms were proposed: one that represents the options explicitly, and the other that does it implicitly. We show that the intrinsic reward used in the latter is subject to bias in stochastic environments, causing convergence to suboptimal solutions. To correct this behavior, we propose two methods respectively based on the transitional probability model and Gaussian Mixture Model. We substantiate our claims through rigorous mathematical derivations and experimental analyses.
true	Disentangling Content and Style via Unsupervised Geometry Distillation generative models unsupervised learning It is challenging to disentangle an object into two orthogonal spaces of content and style since each can influence the visual observation in a different and unpredictable way. It is rare for one to have access to a large number of data to help separate the influences. In this paper, we present a novel framework to learn this disentangled representation in a completely unsupervised manner. We address this problem in a two-branch Autoencoder framework. For the structural content branch, we project the latent factor into a soft structured point tensor and constrain it with losses derived from prior knowledge. This encourages the branch to distill geometry information. Another branch learns the complementary style information. The two branches form an effective framework that can disentangle object's content-style representation without any human annotation. We evaluate our approach on four image datasets, on which we demonstrate the superior disentanglement and visual analogy quality both in synthesized and real-world data. We are able to generate photo-realistic images with 256x256 resolution that are clearly disentangled in content and style.

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.