Datasets:
pwc-archive
/
links-between-paper-and-code

Dataset card Data Studio Files
xet
Community
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https://paperswithcode.com/paper/privacy-issues-in-large-language-models-a
Privacy Issues in Large Language Models: A Survey
2312.06717
https://arxiv.org/abs/2312.06717v4
https://arxiv.org/pdf/2312.06717v4.pdf
https://github.com/safr-ml-lab/survey-llm
true
true
true
tf
https://paperswithcode.com/paper/effects-of-diversity-incentives-on-sample
Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation
2401.06643
https://arxiv.org/abs/2401.06643v3
https://arxiv.org/pdf/2401.06643v3.pdf
https://github.com/kinit-sk/llm-div-incts
true
true
true
pytorch
https://paperswithcode.com/paper/detecting-attended-visual-targets-in-video
Detecting Attended Visual Targets in Video
2003.02501
https://arxiv.org/abs/2003.02501v2
https://arxiv.org/pdf/2003.02501v2.pdf
https://github.com/ejcgt/attention-target-detection
false
true
true
pytorch
https://paperswithcode.com/paper/spatially-adaptive-self-supervised-learning
Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising
2303.14934
https://arxiv.org/abs/2303.14934v1
https://arxiv.org/pdf/2303.14934v1.pdf
https://github.com/nagejacob/spatiallyadaptivessid
true
true
true
pytorch
https://paperswithcode.com/paper/rethinking-node-wise-propagation-for-large
Rethinking Node-wise Propagation for Large-scale Graph Learning
2402.06128
https://arxiv.org/abs/2402.06128v1
https://arxiv.org/pdf/2402.06128v1.pdf
https://github.com/xkli-allen/atp
true
true
false
pytorch
https://paperswithcode.com/paper/multi-granularity-correspondence-learning-1
Multi-granularity Correspondence Learning from Long-term Noisy Videos
2401.16702
https://arxiv.org/abs/2401.16702v1
https://arxiv.org/pdf/2401.16702v1.pdf
https://github.com/XLearning-SCU/2024-ICLR-Norton
false
false
true
pytorch
https://paperswithcode.com/paper/llm4eda-emerging-progress-in-large-language
LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation
2401.12224
https://arxiv.org/abs/2401.12224v1
https://arxiv.org/pdf/2401.12224v1.pdf
https://github.com/thinklab-sjtu/awesome-llm4eda
true
true
true
none
https://paperswithcode.com/paper/training-on-test-proteins-improves-fitness
Training on test proteins improves fitness, structure, and function prediction
2411.02109
https://arxiv.org/abs/2411.02109v1
https://arxiv.org/pdf/2411.02109v1.pdf
https://github.com/anton-bushuiev/ProteinTTT
true
false
true
pytorch
https://paperswithcode.com/paper/structured-complex-and-time-complete-temporal
SCTc-TE: A Comprehensive Formulation and Benchmark for Temporal Event Forecasting
2312.01052
https://arxiv.org/abs/2312.01052v2
https://arxiv.org/pdf/2312.01052v2.pdf
https://github.com/yecchen/gdelt-complexevent
true
true
true
pytorch
https://paperswithcode.com/paper/a-robust-ensemble-algorithm-for-ischemic
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
2403.19425
https://arxiv.org/abs/2403.19425v2
https://arxiv.org/pdf/2403.19425v2.pdf
https://github.com/ezequieldlrosa/isles22
true
true
false
none
https://paperswithcode.com/paper/khronos-a-unified-approach-for-spatio
Khronos: A Unified Approach for Spatio-Temporal Metric-Semantic SLAM in Dynamic Environments
2402.13817
https://arxiv.org/abs/2402.13817v2
https://arxiv.org/pdf/2402.13817v2.pdf
https://github.com/mit-spark/khronos
true
true
true
none
https://paperswithcode.com/paper/scene-graph-generation-from-hierarchical
Hierarchical Relationships: A New Perspective to Enhance Scene Graph Generation
2303.06842
https://arxiv.org/abs/2303.06842v5
https://arxiv.org/pdf/2303.06842v5.pdf
https://github.com/bowen-upenn/scene_graph_commonsense
true
false
true
pytorch
https://paperswithcode.com/paper/a-closed-form-solution-to-best-rank-1-tensor
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation
2103.02898
https://arxiv.org/abs/2103.02898v3
https://arxiv.org/pdf/2103.02898v3.pdf
https://github.com/gkazunii/Legendre-tucker-rank-reduction
true
false
true
none
https://paperswithcode.com/paper/translating-images-to-road-network-a-non-1
Translating Images to Road Network: A Sequence-to-Sequence Perspective
2402.08207
https://arxiv.org/abs/2402.08207v2
https://arxiv.org/pdf/2402.08207v2.pdf
https://github.com/MindSpore-scientific-2/code-3/tree/main/translating-math-formula-images
false
false
false
mindspore
https://paperswithcode.com/paper/activerag-revealing-the-treasures-of
ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents
2402.13547
https://arxiv.org/abs/2402.13547v2
https://arxiv.org/pdf/2402.13547v2.pdf
https://github.com/openmatch/activerag
true
true
true
none
https://paperswithcode.com/paper/instruction-tuned-language-models-are-better
Instruction-tuned Language Models are Better Knowledge Learners
2402.12847
https://arxiv.org/abs/2402.12847v2
https://arxiv.org/pdf/2402.12847v2.pdf
https://github.com/edward-sun/pit
true
true
true
none
https://paperswithcode.com/paper/scale-match-for-tiny-person-detection
Scale Match for Tiny Person Detection
1912.10664
https://arxiv.org/abs/1912.10664v1
https://arxiv.org/pdf/1912.10664v1.pdf
https://github.com/ucas-vg/TinyBenchmark
true
true
true
pytorch
https://paperswithcode.com/paper/object-localization-under-single-coarse-point
Object Localization under Single Coarse Point Supervision
2203.09338
https://arxiv.org/abs/2203.09338v1
https://arxiv.org/pdf/2203.09338v1.pdf
https://github.com/ucas-vg/TinyBenchmark
false
false
true
pytorch
https://paperswithcode.com/paper/point-to-box-network-for-accurate-object
Point-to-Box Network for Accurate Object Detection via Single Point Supervision
2207.06827
https://arxiv.org/abs/2207.06827v2
https://arxiv.org/pdf/2207.06827v2.pdf
https://github.com/ucas-vg/TinyBenchmark
false
false
true
pytorch
https://paperswithcode.com/paper/cpr-object-localization-via-single-coarse
CPR++: Object Localization via Single Coarse Point Supervision
2401.17203
https://arxiv.org/abs/2401.17203v1
https://arxiv.org/pdf/2401.17203v1.pdf
https://github.com/ucas-vg/TinyBenchmark
false
false
true
pytorch
https://paperswithcode.com/paper/caphuman-capture-your-moments-in-parallel
CapHuman: Capture Your Moments in Parallel Universes
2402.00627
https://arxiv.org/abs/2402.00627v3
https://arxiv.org/pdf/2402.00627v3.pdf
https://github.com/vamosc/caphuman
true
true
true
pytorch
https://paperswithcode.com/paper/pap-rec-personalized-automatic-prompt-for
PAP-REC: Personalized Automatic Prompt for Recommendation Language Model
2402.00284
https://arxiv.org/abs/2402.00284v1
https://arxiv.org/pdf/2402.00284v1.pdf
https://github.com/rutgerswiselab/pap-rec
true
true
false
pytorch
https://paperswithcode.com/paper/wordepth-variational-language-prior-for
WorDepth: Variational Language Prior for Monocular Depth Estimation
2404.03635
https://arxiv.org/abs/2404.03635v4
https://arxiv.org/pdf/2404.03635v4.pdf
https://github.com/adonis-galaxy/wordepth
true
true
true
pytorch
https://paperswithcode.com/paper/improving-multimodal-classification-of-social
Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Tasks
2309.07794
https://arxiv.org/abs/2309.07794v2
https://arxiv.org/pdf/2309.07794v2.pdf
https://github.com/danaesavi/socialmedia-textimage-classification-auxlosses
true
true
false
pytorch
https://paperswithcode.com/paper/attention-based-simple-primitives-for-open
Attention Based Simple Primitives for Open World Compositional Zero-Shot Learning
2407.13715
https://arxiv.org/abs/2407.13715v1
https://arxiv.org/pdf/2407.13715v1.pdf
https://github.com/ans92/ASP
true
false
true
pytorch
https://paperswithcode.com/paper/learning-semantic-proxies-from-visual-prompts
Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
2402.02340
https://arxiv.org/abs/2402.02340v2
https://arxiv.org/pdf/2402.02340v2.pdf
https://github.com/noahsark/parameterefficient-dml
true
true
true
pytorch
https://paperswithcode.com/paper/fade-fusing-the-assets-of-decoder-and-encoder
FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling
2207.10392
https://arxiv.org/abs/2207.10392v2
https://arxiv.org/pdf/2207.10392v2.pdf
https://github.com/poppinace/fade
false
false
true
pytorch
https://paperswithcode.com/paper/promptrr-diffusion-models-as-prompt
PromptRR: Diffusion Models as Prompt Generators for Single Image Reflection Removal
2402.02374
https://arxiv.org/abs/2402.02374v1
https://arxiv.org/pdf/2402.02374v1.pdf
https://github.com/taowangzj/promptrr
true
true
true
none
https://paperswithcode.com/paper/boosting-adversarial-transferability-across
Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping
2402.03951
https://arxiv.org/abs/2402.03951v1
https://arxiv.org/pdf/2402.03951v1.pdf
https://github.com/linqinliang/decowa
true
true
true
none
https://paperswithcode.com/paper/learning-to-simulate-complex-physics-with
Learning to Simulate Complex Physics with Graph Networks
2002.09405
https://arxiv.org/abs/2002.09405v2
https://arxiv.org/pdf/2002.09405v2.pdf
https://github.com/tumaer/lagrangebench
false
false
true
jax
https://paperswithcode.com/paper/feedback-loops-with-language-models-drive-in
Feedback Loops With Language Models Drive In-Context Reward Hacking
2402.06627
https://arxiv.org/abs/2402.06627v3
https://arxiv.org/pdf/2402.06627v3.pdf
https://github.com/aypan17/llm-feedback
true
true
true
none
https://paperswithcode.com/paper/how-faithful-is-your-synthetic-data-sample
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
2102.08921
https://arxiv.org/abs/2102.08921v2
https://arxiv.org/pdf/2102.08921v2.pdf
https://github.com/amazon-science/tabsyn
false
false
true
pytorch
https://paperswithcode.com/paper/actor-critic-algorithms-for-fiber-sampling
Learning to sample fibers for goodness-of-fit testing
2405.13950
https://arxiv.org/abs/2405.13950v3
https://arxiv.org/pdf/2405.13950v3.pdf
https://github.com/DLR-RM/stable-baselines3
true
true
false
pytorch
https://paperswithcode.com/paper/knowledge-driven-cross-document-relation
Knowledge-Driven Cross-Document Relation Extraction
2405.13546
https://arxiv.org/abs/2405.13546v2
https://arxiv.org/pdf/2405.13546v2.pdf
https://github.com/kracr/cross-doc-relation-extraction
true
true
false
pytorch
https://paperswithcode.com/paper/neural-optimizer-equation-decay-function-and
Neural Optimizer Equation, Decay Function, and Learning Rate Schedule Joint Evolution
2404.06679
https://arxiv.org/abs/2404.06679v1
https://arxiv.org/pdf/2404.06679v1.pdf
https://github.com/oustudent/neuraloptimizersearch
true
true
false
tf
https://paperswithcode.com/paper/how-to-tune-a-multilingual-encoder-model-for
How to Tune a Multilingual Encoder Model for Germanic Languages: A Study of PEFT, Full Fine-Tuning, and Language Adapters
2501.06025
https://arxiv.org/abs/2501.06025v1
https://arxiv.org/pdf/2501.06025v1.pdf
https://github.com/rominaoji/german-language-adapter
true
true
true
pytorch
https://paperswithcode.com/paper/fails-a-framework-for-automated-collection
FAILS: A Framework for Automated Collection and Analysis of LLM Service Incidents
2503.12185
https://arxiv.org/abs/2503.12185v1
https://arxiv.org/pdf/2503.12185v1.pdf
https://github.com/atlarge-research/fails
true
true
false
none
https://paperswithcode.com/paper/towards-understanding-jailbreak-attacks-in
Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis
2406.10794
https://arxiv.org/abs/2406.10794v3
https://arxiv.org/pdf/2406.10794v3.pdf
https://github.com/yuplin2333/representation-space-jailbreak
true
true
true
pytorch
https://paperswithcode.com/paper/shuttleset-a-human-annotated-stroke-level
ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis
2306.04948
https://arxiv.org/abs/2306.04948v1
https://arxiv.org/pdf/2306.04948v1.pdf
https://github.com/andychiangsh/badge
false
false
true
none
https://paperswithcode.com/paper/ehrnoteqa-a-patient-specific-question
EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries
2402.16040
https://arxiv.org/abs/2402.16040v5
https://arxiv.org/pdf/2402.16040v5.pdf
https://github.com/ji-youn-kim/ehrnoteqa
true
true
true
pytorch
https://paperswithcode.com/paper/fvit-a-focal-vision-transformer-with-gabor
FViT: A Focal Vision Transformer with Gabor Filter
2402.11303
https://arxiv.org/abs/2402.11303v3
https://arxiv.org/pdf/2402.11303v3.pdf
https://github.com/nkusyl/fvit
true
true
true
pytorch
https://paperswithcode.com/paper/generative-3d-part-assembly-via-part-whole
Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing
2402.17464
https://arxiv.org/abs/2402.17464v3
https://arxiv.org/pdf/2402.17464v3.pdf
https://github.com/pkudba/3dhpa
true
true
false
pytorch
https://paperswithcode.com/paper/pcr-99-a-practical-method-for-point-cloud
PCR-99: A Practical Method for Point Cloud Registration with 99 Percent Outliers
2402.16598
https://arxiv.org/abs/2402.16598v6
https://arxiv.org/pdf/2402.16598v6.pdf
https://github.com/sunghoon031/pcr-99
true
true
true
none
https://paperswithcode.com/paper/a-framework-for-standardizing-similarity
A Framework for Standardizing Similarity Measures in a Rapidly Evolving Field
2409.18333
https://arxiv.org/abs/2409.18333v1
https://arxiv.org/pdf/2409.18333v1.pdf
https://github.com/nacloos/similarity-repository
true
false
false
pytorch
https://paperswithcode.com/paper/soul-unlocking-the-power-of-second-order
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning
2404.18239
https://arxiv.org/abs/2404.18239v4
https://arxiv.org/pdf/2404.18239v4.pdf
https://github.com/optml-group/soul
true
true
true
none
https://paperswithcode.com/paper/label-informed-contrastive-pretraining-for
Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs
2402.17791
https://arxiv.org/abs/2402.17791v1
https://arxiv.org/pdf/2402.17791v1.pdf
https://github.com/zhangtia16/licap
true
true
true
pytorch
https://paperswithcode.com/paper/univs-unified-and-universal-video
UniVS: Unified and Universal Video Segmentation with Prompts as Queries
2402.18115
https://arxiv.org/abs/2402.18115v2
https://arxiv.org/pdf/2402.18115v2.pdf
https://github.com/minghanli/univs
true
true
true
pytorch
https://paperswithcode.com/paper/rethinking-centered-kernel-alignment-in
Rethinking Centered Kernel Alignment in Knowledge Distillation
2401.11824
https://arxiv.org/abs/2401.11824v4
https://arxiv.org/pdf/2401.11824v4.pdf
https://github.com/klayand/pcka
true
true
false
none
https://paperswithcode.com/paper/retaining-key-information-under-high
Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs
2406.02376
https://arxiv.org/abs/2406.02376v2
https://arxiv.org/pdf/2406.02376v2.pdf
https://github.com/DeepLearnXMU/QGC
true
true
false
pytorch
https://paperswithcode.com/paper/sonata-self-supervised-learning-of-reliable
Sonata: Self-Supervised Learning of Reliable Point Representations
2503.16429
https://arxiv.org/abs/2503.16429v1
https://arxiv.org/pdf/2503.16429v1.pdf
https://github.com/facebookresearch/sonata
true
false
true
pytorch
https://paperswithcode.com/paper/dimal-deep-isometric-manifold-learning-using
DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling
1711.06011
http://arxiv.org/abs/1711.06011v2
http://arxiv.org/pdf/1711.06011v2.pdf
https://github.com/paigautam/DIMAL
true
false
true
pytorch
https://paperswithcode.com/paper/mila-multi-view-intensive-fidelity-long-term
MiLA: Multi-view Intensive-fidelity Long-term Video Generation World Model for Autonomous Driving
2503.15875
https://arxiv.org/abs/2503.15875v1
https://arxiv.org/pdf/2503.15875v1.pdf
https://github.com/xiaomi-mlab/mila.github.io
true
true
false
none
https://paperswithcode.com/paper/fast-graph-condensation-with-structure-based
Fast Graph Condensation with Structure-based Neural Tangent Kernel
2310.11046
https://arxiv.org/abs/2310.11046v2
https://arxiv.org/pdf/2310.11046v2.pdf
https://github.com/wanglin0126/gcsntk
true
true
false
pytorch
https://paperswithcode.com/paper/usat-a-universal-speaker-adaptive-text-to
USAT: A Universal Speaker-Adaptive Text-to-Speech Approach
2404.18094
https://arxiv.org/abs/2404.18094v1
https://arxiv.org/pdf/2404.18094v1.pdf
https://github.com/mushanshanshan/esltts
true
true
true
none
https://paperswithcode.com/paper/safe-deep-model-based-reinforcement-learning
Safe Deep Model-Based Reinforcement Learning with Lyapunov Functions
2405.16184
https://arxiv.org/abs/2405.16184v1
https://arxiv.org/pdf/2405.16184v1.pdf
https://github.com/harryzhangOG/salved
true
true
false
tf
https://paperswithcode.com/paper/clip-ebc-clip-can-count-accurately-through
CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
2403.09281
https://arxiv.org/abs/2403.09281v3
https://arxiv.org/pdf/2403.09281v3.pdf
https://github.com/Yiming-M/CLIP-EBC
true
true
true
pytorch
https://paperswithcode.com/paper/3am-an-ambiguity-aware-multi-modal-machine
3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset
2404.18413
https://arxiv.org/abs/2404.18413v1
https://arxiv.org/pdf/2404.18413v1.pdf
https://github.com/maxylee/3am
true
true
false
none
https://paperswithcode.com/paper/oodrobustbench-benchmarking-and-analyzing
OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift
2310.12793
https://arxiv.org/abs/2310.12793v2
https://arxiv.org/pdf/2310.12793v2.pdf
https://github.com/treelli/apt
false
false
true
pytorch
https://paperswithcode.com/paper/what-do-we-learn-from-inverting-clip-models
What do we learn from inverting CLIP models?
2403.02580
https://arxiv.org/abs/2403.02580v1
https://arxiv.org/pdf/2403.02580v1.pdf
https://github.com/hamidkazemi22/clipinversion
true
true
true
pytorch
https://paperswithcode.com/paper/xoftr-cross-modal-feature-matching
XoFTR: Cross-modal Feature Matching Transformer
2404.09692
https://arxiv.org/abs/2404.09692v1
https://arxiv.org/pdf/2404.09692v1.pdf
https://github.com/ondert/xoftr
true
true
true
pytorch
https://paperswithcode.com/paper/actor-identified-spatiotemporal-action
Actor-identified Spatiotemporal Action Detection --- Detecting Who Is Doing What in Videos
2208.12940
https://arxiv.org/abs/2208.12940v2
https://arxiv.org/pdf/2208.12940v2.pdf
https://github.com/fandulu/asad
true
true
true
none
https://paperswithcode.com/paper/inverse-decision-making-using-neural
Inverse decision-making using neural amortized Bayesian actors
2409.03710
https://arxiv.org/abs/2409.03710v2
https://arxiv.org/pdf/2409.03710v2.pdf
https://github.com/rothkopflab/naba
true
true
false
jax
https://paperswithcode.com/paper/approach-to-predicting-news-a-precise-multi
Approach to Predicting News -- A Precise Multi-LSTM Network With BERT
2204.12093
https://arxiv.org/abs/2204.12093v1
https://arxiv.org/pdf/2204.12093v1.pdf
https://github.com/LanaChen0/Predict_News
true
true
true
tf
https://paperswithcode.com/paper/language-model-adaptation-to-specialized
Language Model Adaptation to Specialized Domains through Selective Masking based on Genre and Topical Characteristics
2402.12036
https://arxiv.org/abs/2402.12036v2
https://arxiv.org/pdf/2402.12036v2.pdf
https://github.com/ygorg/legal-masking
true
true
true
pytorch
https://paperswithcode.com/paper/rephrase-and-respond-let-large-language
Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
2311.04205
https://arxiv.org/abs/2311.04205v2
https://arxiv.org/pdf/2311.04205v2.pdf
https://github.com/xirui-li/drattack
false
false
true
none
https://paperswithcode.com/paper/data-filtering-networks
Data Filtering Networks
2309.17425
https://arxiv.org/abs/2309.17425v3
https://arxiv.org/pdf/2309.17425v3.pdf
https://github.com/apple/ml-mobileclip
false
false
true
pytorch
https://paperswithcode.com/paper/sigmoid-loss-for-language-image-pre-training
Sigmoid Loss for Language Image Pre-Training
2303.15343
https://arxiv.org/abs/2303.15343v4
https://arxiv.org/pdf/2303.15343v4.pdf
https://github.com/apple/ml-mobileclip
false
false
true
pytorch
https://paperswithcode.com/paper/learning-transferable-visual-models-from
Learning Transferable Visual Models From Natural Language Supervision
2103.00020
https://arxiv.org/abs/2103.00020v1
https://arxiv.org/pdf/2103.00020v1.pdf
https://github.com/apple/ml-mobileclip
false
false
true
pytorch
https://paperswithcode.com/paper/llm-as-os-llmao-agents-as-apps-envisioning
LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem
2312.03815
https://arxiv.org/abs/2312.03815v2
https://arxiv.org/pdf/2312.03815v2.pdf
https://github.com/agiresearch/aios
false
false
true
none
https://paperswithcode.com/paper/on-the-convergence-of-locally-adaptive-and
On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors
2403.08609
https://arxiv.org/abs/2403.08609v2
https://arxiv.org/pdf/2403.08609v2.pdf
https://github.com/timrensmeyer/Convergence-Experiments
true
true
false
pytorch
https://paperswithcode.com/paper/tetrasphere-a-neural-descriptor-for-o-3
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis
2211.14456
https://arxiv.org/abs/2211.14456v6
https://arxiv.org/pdf/2211.14456v6.pdf
https://github.com/pavlo-melnyk/tetrasphere
true
true
true
pytorch
https://paperswithcode.com/paper/generative-ensemble-deep-learning-severe
Generative ensemble deep learning severe weather prediction from a deterministic convection-allowing model
2310.06045
https://arxiv.org/abs/2310.06045v2
https://arxiv.org/pdf/2310.06045v2.pdf
https://github.com/yingkaisha/aies_d_23_0094
true
true
true
tf
https://paperswithcode.com/paper/balancing-act-constraining-disparate-impact
Balancing Act: Constraining Disparate Impact in Sparse Models
2310.20673
https://arxiv.org/abs/2310.20673v2
https://arxiv.org/pdf/2310.20673v2.pdf
https://github.com/merajhashemi/balancing-act
true
true
true
pytorch
https://paperswithcode.com/paper/pathfinding-future-pim-architectures-by
Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology
2308.00846
https://arxiv.org/abs/2308.00846v3
https://arxiv.org/pdf/2308.00846v3.pdf
https://github.com/via-research/upimulator
true
true
true
none
https://paperswithcode.com/paper/core-llm-as-interpreter-for-natural-language
AIOS Compiler: LLM as Interpreter for Natural Language Programming and Flow Programming of AI Agents
2405.06907
https://arxiv.org/abs/2405.06907v2
https://arxiv.org/pdf/2405.06907v2.pdf
https://github.com/agiresearch/aios
true
true
false
none
https://paperswithcode.com/paper/contextual-learning-in-fourier-complex-field
Contextual Learning in Fourier Complex Field for VHR Remote Sensing Images
2210.15972
https://arxiv.org/abs/2210.15972v1
https://arxiv.org/pdf/2210.15972v1.pdf
https://github.com/MindCode-4/code-11/tree/main/contextual-learning
false
false
false
mindspore
https://paperswithcode.com/paper/approaching-test-time-augmentation-in-the
Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks
2304.05104
https://arxiv.org/abs/2304.05104v2
https://arxiv.org/pdf/2304.05104v2.pdf
https://github.com/pedrormconde/mv-atta
true
true
true
pytorch
https://paperswithcode.com/paper/orco-towards-better-generalization-via
OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning
2403.18550
https://arxiv.org/abs/2403.18550v1
https://arxiv.org/pdf/2403.18550v1.pdf
https://github.com/noorahmedds/orco
true
true
true
pytorch
https://paperswithcode.com/paper/computational-sentence-level-metrics
Computational Sentence-level Metrics Predicting Human Sentence Comprehension
2403.15822
https://arxiv.org/abs/2403.15822v2
https://arxiv.org/pdf/2403.15822v2.pdf
https://github.com/fivehills/sentence-relevance-and-sentence-surprisal
true
true
false
none
https://paperswithcode.com/paper/homogeneous-tokenizer-matters-homogeneous
Homogeneous Tokenizer Matters: Homogeneous Visual Tokenizer for Remote Sensing Image Understanding
2403.18593
https://arxiv.org/abs/2403.18593v2
https://arxiv.org/pdf/2403.18593v2.pdf
https://github.com/geox-lab/hook
true
true
true
pytorch
https://paperswithcode.com/paper/deepsdf-learning-continuous-signed-distance
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
1901.05103
http://arxiv.org/abs/1901.05103v1
http://arxiv.org/pdf/1901.05103v1.pdf
https://github.com/maurock/deepsdf
false
false
true
pytorch
https://paperswithcode.com/paper/clip-fields-weakly-supervised-semantic-fields
CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory
2210.05663
https://arxiv.org/abs/2210.05663v3
https://arxiv.org/pdf/2210.05663v3.pdf
https://github.com/notmahi/clip-fields
true
false
true
pytorch
https://paperswithcode.com/paper/diffusionface-towards-a-comprehensive-dataset
DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis
2403.18471
https://arxiv.org/abs/2403.18471v1
https://arxiv.org/pdf/2403.18471v1.pdf
https://github.com/rapisurazurite/diffface
true
true
false
none
https://paperswithcode.com/paper/unprocessing-seven-years-of-algorithmic
Unprocessing Seven Years of Algorithmic Fairness
2306.07261
https://arxiv.org/abs/2306.07261v5
https://arxiv.org/pdf/2306.07261v5.pdf
https://github.com/socialfoundations/error-parity
true
true
true
none
https://paperswithcode.com/paper/weight-inherited-distillation-for-task
Weight-Inherited Distillation for Task-Agnostic BERT Compression
2305.09098
https://arxiv.org/abs/2305.09098v2
https://arxiv.org/pdf/2305.09098v2.pdf
https://github.com/wutaiqiang/WID-NAACL2024
true
true
true
pytorch
https://paperswithcode.com/paper/a-structural-text-based-scaling-model-for
A Structural Text-Based Scaling Model for Analyzing Political Discourse
2410.11897
https://arxiv.org/abs/2410.11897v1
https://arxiv.org/pdf/2410.11897v1.pdf
https://github.com/vavrajan/stbs
true
true
true
tf
https://paperswithcode.com/paper/nach0-multimodal-natural-and-chemical
nach0: Multimodal Natural and Chemical Languages Foundation Model
2311.12410
https://arxiv.org/abs/2311.12410v3
https://arxiv.org/pdf/2311.12410v3.pdf
https://github.com/insilicomedicine/nach0
true
true
true
none
https://paperswithcode.com/paper/decode-neural-signal-as-speech
NeuSpeech: Decode Neural signal as Speech
2403.01748
https://arxiv.org/abs/2403.01748v3
https://arxiv.org/pdf/2403.01748v3.pdf
https://github.com/mikewangwzhl/eeg-to-text
false
false
true
pytorch
https://paperswithcode.com/paper/gptscore-evaluate-as-you-desire
GPTScore: Evaluate as You Desire
2302.04166
https://arxiv.org/abs/2302.04166v2
https://arxiv.org/pdf/2302.04166v2.pdf
https://github.com/osu-nlp-group/llm-cn-eval
false
false
true
pytorch
https://paperswithcode.com/paper/using-pre-trained-language-models-for
Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study
2204.01440
https://arxiv.org/abs/2204.01440v1
https://arxiv.org/pdf/2204.01440v1.pdf
https://github.com/osu-nlp-group/llm-cn-eval
false
false
true
pytorch
https://paperswithcode.com/paper/mmoe-mixture-of-multimodal-interaction
MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts
2311.09580
https://arxiv.org/abs/2311.09580v3
https://arxiv.org/pdf/2311.09580v3.pdf
https://github.com/lwaekfjlk/mmoe
true
true
true
pytorch
https://paperswithcode.com/paper/on-interference-rejection-using-riemannian
On Interference-Rejection Using Riemannian Geometry for Direction of Arrival Estimation
2301.03399
https://arxiv.org/abs/2301.03399v2
https://arxiv.org/pdf/2301.03399v2.pdf
https://github.com/amitaybar/interference-rejection-using-riemannian-geometry-for-doa-estimation
true
true
false
none
https://paperswithcode.com/paper/integrate-the-essence-and-eliminate-the-dross
Integrate the Essence and Eliminate the Dross: Fine-Grained Self-Consistency for Free-Form Language Generation
2407.02056
https://arxiv.org/abs/2407.02056v1
https://arxiv.org/pdf/2407.02056v1.pdf
https://github.com/WangXinglin/FSC
true
true
true
none
https://paperswithcode.com/paper/aetta-label-free-accuracy-estimation-for-test
AETTA: Label-Free Accuracy Estimation for Test-Time Adaptation
2404.01351
https://arxiv.org/abs/2404.01351v1
https://arxiv.org/pdf/2404.01351v1.pdf
https://github.com/taeckyung/aetta
true
true
false
pytorch
https://paperswithcode.com/paper/age-of-information-in-prioritized-random
Age of Information in Prioritized Random Access
2112.01182
https://arxiv.org/abs/2112.01182v1
https://arxiv.org/pdf/2112.01182v1.pdf
https://github.com/khachoang1412/AoI_prioritized_random_access
true
false
false
none
https://paperswithcode.com/paper/quantifying-distribution-shifts-and
Quantifying Distribution Shifts and Uncertainties for Enhanced Model Robustness in Machine Learning Applications
2405.01978
https://arxiv.org/abs/2405.01978v1
https://arxiv.org/pdf/2405.01978v1.pdf
https://github.com/veflo/uncert_quant
true
true
true
tf
https://paperswithcode.com/paper/scalable-3d-registration-via-truncated-entry
Scalable 3D Registration via Truncated Entry-wise Absolute Residuals
2404.00915
https://arxiv.org/abs/2404.00915v2
https://arxiv.org/pdf/2404.00915v2.pdf
https://github.com/tyhuang98/tear-release
true
true
false
none
https://paperswithcode.com/paper/pixel-wise-agricultural-image-time-series
Pixel-wise Agricultural Image Time Series Classification: Comparisons and a Deformable Prototype-based Approach
2303.12533
https://arxiv.org/abs/2303.12533v2
https://arxiv.org/pdf/2303.12533v2.pdf
https://github.com/elliotvincent/agriitsc
true
true
true
pytorch
https://paperswithcode.com/paper/comparing-personalized-relevance-algorithms
Comparing Personalized Relevance Algorithms for Directed Graphs
2405.02261
https://arxiv.org/abs/2405.02261v1
https://arxiv.org/pdf/2405.02261v1.pdf
https://github.com/cyclerank/cyclerank-demo
true
true
false
none
https://paperswithcode.com/paper/nuqmm-quantized-matmul-for-efficient
LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
2206.09557
https://arxiv.org/abs/2206.09557v4
https://arxiv.org/pdf/2206.09557v4.pdf
https://github.com/naver-aics/lut-gemm
true
true
true
none