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

Dataset card Data Studio Files
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Community
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https://paperswithcode.com/paper/a-deep-branching-solver-for-fully-nonlinear
A deep branching solver for fully nonlinear partial differential equations
2203.03234
https://arxiv.org/abs/2203.03234v2
https://arxiv.org/pdf/2203.03234v2.pdf
https://github.com/nguwijy/deep_branching
true
true
false
pytorch
https://paperswithcode.com/paper/dependency-parsing-as-mrc-based-span-span
Dependency Parsing as MRC-based Span-Span Prediction
2105.07654
https://arxiv.org/abs/2105.07654v4
https://arxiv.org/pdf/2105.07654v4.pdf
https://github.com/ShannonAI/mrc-for-dependency-parsing
true
true
true
pytorch
https://paperswithcode.com/paper/infinitely-divisible-noise-in-the-low-privacy
Infinitely Divisible Noise in the Low Privacy Regime
2110.06559
https://arxiv.org/abs/2110.06559v3
https://arxiv.org/pdf/2110.06559v3.pdf
https://github.com/rasmus-pagh/alt22-code
true
false
false
none
https://paperswithcode.com/paper/universal-early-warning-signals-of-phase
Universal Early Warning Signals of Phase Transitions in Climate Systems
2206.00060
https://arxiv.org/abs/2206.00060v2
https://arxiv.org/pdf/2206.00060v2.pdf
https://github.com/dylewsky/phase_transition_ews
true
true
false
tf
https://paperswithcode.com/paper/a-bregman-learning-framework-for-sparse
A Bregman Learning Framework for Sparse Neural Networks
2105.04319
https://arxiv.org/abs/2105.04319v3
https://arxiv.org/pdf/2105.04319v3.pdf
https://github.com/TimRoith/BregmanLearning
true
true
true
pytorch
https://paperswithcode.com/paper/two-finite-element-approaches-for-the-porous
Two Finite Element Approaches For The Porous Medium Equation That Are Positivity Preserving And Energy Stable
2303.14216
https://arxiv.org/abs/2303.14216v1
https://arxiv.org/pdf/2303.14216v1.pdf
https://github.com/avj-jpg/pme
true
true
false
none
https://paperswithcode.com/paper/argscichat-a-dataset-for-argumentative
ArgSciChat: A Dataset for Argumentative Dialogues on Scientific Papers
2202.06690
https://arxiv.org/abs/2202.06690v3
https://arxiv.org/pdf/2202.06690v3.pdf
https://github.com/ukplab/arxiv2022-argscichat
true
true
true
none
https://paperswithcode.com/paper/simple-baselines-for-image-restoration
Simple Baselines for Image Restoration
2204.04676
https://arxiv.org/abs/2204.04676v4
https://arxiv.org/pdf/2204.04676v4.pdf
https://github.com/dslisleedh/NAFNet-flax
false
false
true
jax
https://paperswithcode.com/paper/diffusion-probabilistic-models-for-scene
Diffusion Probabilistic Models for Scene-Scale 3D Categorical Data
2301.00527
https://arxiv.org/abs/2301.00527v1
https://arxiv.org/pdf/2301.00527v1.pdf
https://github.com/zoomin-lee/scene-scale-diffusion
true
true
true
pytorch
https://paperswithcode.com/paper/video-waterdrop-removal-via-spatio-temporal
Video Waterdrop Removal via Spatio-Temporal Fusion in Driving Scenes
2302.05916
https://arxiv.org/abs/2302.05916v3
https://arxiv.org/pdf/2302.05916v3.pdf
https://github.com/csqiangwen/Video_Waterdrop_Removal_in_Driving_Scenes
false
false
true
pytorch
https://paperswithcode.com/paper/quantum-error-mitigation
Quantum Error Mitigation
2210.00921
https://arxiv.org/abs/2210.00921v3
https://arxiv.org/pdf/2210.00921v3.pdf
https://github.com/fbm2718/QREM
true
true
false
none
https://paperswithcode.com/paper/improved-error-estimate-for-the-order-of
Strong order-one convergence of the Euler method for random ordinary differential equations driven by semi-martingale noises
2306.15418
https://arxiv.org/abs/2306.15418v7
https://arxiv.org/pdf/2306.15418v7.pdf
https://github.com/rmsrosa/rode_conv_em
true
true
false
none
https://paperswithcode.com/paper/tensor-train-thermo-field-memory-kernels-for
Tensor-Train Thermo-Field Memory Kernels for Generalized Quantum Master Equations
2208.14273
https://arxiv.org/abs/2208.14273v2
https://arxiv.org/pdf/2208.14273v2.pdf
https://github.com/ningyilyu/tt-tfd-gqme
true
true
false
none
https://paperswithcode.com/paper/instruction-clarification-requests-in
Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw Dataset
2302.14406
https://arxiv.org/abs/2302.14406v1
https://arxiv.org/pdf/2302.14406v1.pdf
https://github.com/briemadu/codraw-icr-v1
true
true
false
pytorch
https://paperswithcode.com/paper/icnet-for-real-time-semantic-segmentation-on
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
1704.08545
http://arxiv.org/abs/1704.08545v2
http://arxiv.org/pdf/1704.08545v2.pdf
https://github.com/Bigpingping97/ICNet
false
false
true
mindspore
https://paperswithcode.com/paper/mntts-an-open-source-mongolian-text-to-speech
MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied Baseline
2209.10848
https://arxiv.org/abs/2209.10848v1
https://arxiv.org/pdf/2209.10848v1.pdf
https://github.com/walker-hyf/mntts
true
true
true
tf
https://paperswithcode.com/paper/padl-language-directed-physics-based
PADL: Language-Directed Physics-Based Character Control
2301.13868
https://arxiv.org/abs/2301.13868v1
https://arxiv.org/pdf/2301.13868v1.pdf
https://github.com/nv-tlabs/padl
true
true
false
none
https://paperswithcode.com/paper/controllable-3d-generative-adversarial-face
Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance
2208.14263
https://arxiv.org/abs/2208.14263v1
https://arxiv.org/pdf/2208.14263v1.pdf
https://github.com/aashishrai3799/3DFaceCAM
true
false
true
pytorch
https://paperswithcode.com/paper/mask-r-cnn
Mask R-CNN
1703.06870
http://arxiv.org/abs/1703.06870v3
http://arxiv.org/pdf/1703.06870v3.pdf
https://github.com/tensorflow/models/tree/master/official/vision
false
false
false
tf
https://paperswithcode.com/paper/metric-learning-and-adaptive-boundary-for-out
Metric Learning and Adaptive Boundary for Out-of-Domain Detection
2204.10849
https://arxiv.org/abs/2204.10849v1
https://arxiv.org/pdf/2204.10849v1.pdf
https://github.com/tgargiani/adaptive-boundary
true
true
true
tf
https://paperswithcode.com/paper/mvd-memory-related-vulnerability-detection
MVD: Memory-Related Vulnerability Detection Based on Flow-Sensitive Graph Neural Networks
2203.02660
https://arxiv.org/abs/2203.02660v1
https://arxiv.org/pdf/2203.02660v1.pdf
https://github.com/MindCode-4/code-14/tree/main/MVD
false
false
false
mindspore
https://paperswithcode.com/paper/randomrooms-unsupervised-pre-training-from
RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection
2108.07794
https://arxiv.org/abs/2108.07794v1
https://arxiv.org/pdf/2108.07794v1.pdf
https://github.com/xuxw98/backtoreality
false
false
true
pytorch
https://paperswithcode.com/paper/back-to-reality-weakly-supervised-3d-object
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement
2203.05238
https://arxiv.org/abs/2203.05238v3
https://arxiv.org/pdf/2203.05238v3.pdf
https://github.com/xuxw98/backtoreality
true
true
true
pytorch
https://paperswithcode.com/paper/adaptive-positive-unlabelled-learning-via
NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification
2108.06158
https://arxiv.org/abs/2108.06158v4
https://arxiv.org/pdf/2108.06158v4.pdf
https://github.com/andmastro/niapu
true
true
false
none
https://paperswithcode.com/paper/rethinking-degradation-radiograph-super
Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN
2208.03008
https://arxiv.org/abs/2208.03008v1
https://arxiv.org/pdf/2208.03008v1.pdf
https://github.com/yongsongh/aidsrgan-miccai2022
true
true
true
pytorch
https://paperswithcode.com/paper/variational-deep-embedding-an-unsupervised
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
1611.05148
http://arxiv.org/abs/1611.05148v3
http://arxiv.org/pdf/1611.05148v3.pdf
https://github.com/lupalab/posterior-matching
false
false
true
jax
https://paperswithcode.com/paper/very-deep-vaes-generalize-autoregressive-1
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
2011.10650
https://arxiv.org/abs/2011.10650v2
https://arxiv.org/pdf/2011.10650v2.pdf
https://github.com/lupalab/posterior-matching
false
false
true
jax
https://paperswithcode.com/paper/neural-discrete-representation-learning
Neural Discrete Representation Learning
1711.00937
http://arxiv.org/abs/1711.00937v2
http://arxiv.org/pdf/1711.00937v2.pdf
https://github.com/lupalab/posterior-matching
false
false
true
jax
https://paperswithcode.com/paper/learning-generative-structure-prior-for-blind
Learning Generative Structure Prior for Blind Text Image Super-resolution
2303.14726
https://arxiv.org/abs/2303.14726v1
https://arxiv.org/pdf/2303.14726v1.pdf
https://github.com/csxmli2016/marconet
true
true
true
pytorch
https://paperswithcode.com/paper/fast-lio-a-fast-robust-lidar-inertial
FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter
2010.08196
https://arxiv.org/abs/2010.08196v3
https://arxiv.org/pdf/2010.08196v3.pdf
https://github.com/hku-mars/FAST_LIO
true
true
true
none
https://paperswithcode.com/paper/llama-open-and-efficient-foundation-language-1
LLaMA: Open and Efficient Foundation Language Models
2302.13971
https://arxiv.org/abs/2302.13971v1
https://arxiv.org/pdf/2302.13971v1.pdf
https://github.com/MS-P3/code5/tree/main/llama2
false
false
false
mindspore
https://paperswithcode.com/paper/robust-and-online-lidar-inertial
Robust Real-time LiDAR-inertial Initialization
2202.11006
https://arxiv.org/abs/2202.11006v5
https://arxiv.org/pdf/2202.11006v5.pdf
https://github.com/hku-mars/FAST_LIO
false
false
true
none
https://paperswithcode.com/paper/pre-training-of-temporal-convolutional-neural
PREP: Pre-training with Temporal Elapse Inference for Popularity Prediction
2108.06220
https://arxiv.org/abs/2108.06220v2
https://arxiv.org/pdf/2108.06220v2.pdf
https://github.com/caoqi92/prep
true
true
true
pytorch
https://paperswithcode.com/paper/when-did-you-become-so-smart-oh-wise-one-1
When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues
2203.06419
https://arxiv.org/abs/2203.06419v1
https://arxiv.org/pdf/2203.06419v1.pdf
https://github.com/lcs2-iiitd/maf
true
true
true
pytorch
https://paperswithcode.com/paper/fastkassim-a-fast-tree-kernel-based-syntactic
FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric
2203.08299
https://arxiv.org/abs/2203.08299v4
https://arxiv.org/pdf/2203.08299v4.pdf
https://github.com/jasonyux/fastkassim
true
true
true
none
https://paperswithcode.com/paper/learning-from-multiple-annotator-noisy-labels
Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
2207.11327
https://arxiv.org/abs/2207.11327v1
https://arxiv.org/pdf/2207.11327v1.pdf
https://github.com/zhengqigao/learning-from-multiple-annotator-noisy-labels
true
true
false
pytorch
https://paperswithcode.com/paper/darts-differentiable-architecture-search
DARTS: Differentiable Architecture Search
1806.09055
http://arxiv.org/abs/1806.09055v2
http://arxiv.org/pdf/1806.09055v2.pdf
https://github.com/alphadl/darts.pytorch1.1
false
false
true
pytorch
https://paperswithcode.com/paper/approximate-localised-dihedral-patterns-near
Approximate localised dihedral patterns near a Turing instability
2203.09363
https://arxiv.org/abs/2203.09363v2
https://arxiv.org/pdf/2203.09363v2.pdf
https://github.com/Dan-Hill95/Localised-Dihedral-Patterns
true
true
true
none
https://paperswithcode.com/paper/stomanager1-automated-high-throughput-tool-to
StoManager1: An Enhanced, Automated, and High-throughput Tool to Measure Leaf Stomata and Guard Cell Metrics Using Empirical and Theoretical Algorithms
2304.10450
https://arxiv.org/abs/2304.10450v3
https://arxiv.org/pdf/2304.10450v3.pdf
https://github.com/JiaxinWang123/StoManager1
true
false
false
pytorch
https://paperswithcode.com/paper/slam-tka-real-time-intra-operative
SLAM-TKA: Real-time Intra-operative Measurement of Tibial Resection Plane in Conventional Total Knee Arthroplasty
2208.03945
https://arxiv.org/abs/2208.03945v1
https://arxiv.org/pdf/2208.03945v1.pdf
https://github.com/zsustc/calibration
true
true
false
none
https://paperswithcode.com/paper/gluonts-probabilistic-time-series-models-in
GluonTS: Probabilistic Time Series Models in Python
1906.05264
https://arxiv.org/abs/1906.05264v2
https://arxiv.org/pdf/1906.05264v2.pdf
https://github.com/WLM1ke/poptimizer
false
false
true
pytorch
https://paperswithcode.com/paper/layered-rendering-diffusion-model-for-zero
Layered Rendering Diffusion Model for Controllable Zero-Shot Image Synthesis
2311.18435
https://arxiv.org/abs/2311.18435v2
https://arxiv.org/pdf/2311.18435v2.pdf
https://github.com/syang-lab/layered_rendering_diffusion_model
false
false
false
pytorch
https://paperswithcode.com/paper/attention-is-all-you-need
Attention Is All You Need
1706.03762
https://arxiv.org/abs/1706.03762v7
https://arxiv.org/pdf/1706.03762v7.pdf
https://github.com/harenlin/IMDB-Sentiment-Analysis-Using-BERT-Fine-Tuning
false
false
true
tf
https://paperswithcode.com/paper/weakly-supervised-salient-object-detection-2
Weakly-Supervised Salient Object Detection Using Point Supervision
2203.11652
https://arxiv.org/abs/2203.11652v2
https://arxiv.org/pdf/2203.11652v2.pdf
https://github.com/shuyonggao/psod
true
true
false
pytorch
https://paperswithcode.com/paper/hierarchically-coordinated-energy-management
Hierarchically Coordinated Energy Management for A Regional Multi-microgrid Community
2102.03745
https://arxiv.org/abs/2102.03745v1
https://arxiv.org/pdf/2102.03745v1.pdf
https://github.com/juchengquan/Hierarchically_Coordinated_Energy_Management_for_A_Regional_Multi-microgrid_Community
true
false
true
none
https://paperswithcode.com/paper/bert-pre-training-of-deep-bidirectional
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
1810.04805
https://arxiv.org/abs/1810.04805v2
https://arxiv.org/pdf/1810.04805v2.pdf
https://github.com/harenlin/IMDB-Sentiment-Analysis-Using-BERT-Fine-Tuning
false
false
true
tf
https://paperswithcode.com/paper/spinenet-learning-scale-permuted-backbone-for
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
1912.05027
https://arxiv.org/abs/1912.05027v3
https://arxiv.org/pdf/1912.05027v3.pdf
https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet152
false
false
false
mindspore
https://paperswithcode.com/paper/developing-distributed-high-performance
Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis
2304.14244
https://arxiv.org/abs/2304.14244v2
https://arxiv.org/pdf/2304.14244v2.pdf
https://github.com/nsf-resume/2023_parsocial_osprey_example
true
true
false
none
https://paperswithcode.com/paper/converting-admm-to-a-proximal-gradient-for
Converting ADMM to a Proximal Gradient for Efficient Sparse Estimation
2104.10911
https://arxiv.org/abs/2104.10911v3
https://arxiv.org/pdf/2104.10911v3.pdf
https://github.com/theveni/scc_tf
true
true
true
none
https://paperswithcode.com/paper/gazesam-what-you-see-is-what-you-segment
GazeSAM: What You See is What You Segment
2304.13844
https://arxiv.org/abs/2304.13844v1
https://arxiv.org/pdf/2304.13844v1.pdf
https://github.com/ukaukaaaa/gazesam
true
true
true
pytorch
https://paperswithcode.com/paper/modeling-users-contextualized-page-wise
Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
2203.15542
https://arxiv.org/abs/2203.15542v1
https://arxiv.org/pdf/2203.15542v1.pdf
https://github.com/racp-submission/racp
true
true
false
pytorch
https://paperswithcode.com/paper/generating-self-contained-and-summary-centric
Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning
2109.04689
https://arxiv.org/abs/2109.04689v1
https://arxiv.org/pdf/2109.04689v1.pdf
https://github.com/amazon-research/sc2qa-dril
true
true
true
pytorch
https://paperswithcode.com/paper/self-supervised-leaf-segmentation-under
Self-Supervised Leaf Segmentation under Complex Lighting Conditions
2203.15943
https://arxiv.org/abs/2203.15943v1
https://arxiv.org/pdf/2203.15943v1.pdf
https://github.com/lxfhfut/self-supervised-leaf-segmentation
true
true
false
pytorch
https://paperswithcode.com/paper/semantic-line-detection-using-mirror-1
Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching
2203.15285
https://arxiv.org/abs/2203.15285v1
https://arxiv.org/pdf/2203.15285v1.pdf
https://github.com/dongkwonjin/Semantic-Line-DRM
true
true
true
pytorch
https://paperswithcode.com/paper/mplug-owl-modularization-empowers-large
mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
2304.14178
https://arxiv.org/abs/2304.14178v3
https://arxiv.org/pdf/2304.14178v3.pdf
https://github.com/x-plug/mplug-owl
true
true
true
pytorch
https://paperswithcode.com/paper/collaborative-transformers-for-grounded
Collaborative Transformers for Grounded Situation Recognition
2203.16518
https://arxiv.org/abs/2203.16518v1
https://arxiv.org/pdf/2203.16518v1.pdf
https://github.com/jhcho99/coformer
true
true
true
pytorch
https://paperswithcode.com/paper/conditions-for-estimation-of-sensitivities-of
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power Injections
2212.01471
https://arxiv.org/abs/2212.01471v2
https://arxiv.org/pdf/2212.01471v2.pdf
https://github.com/samtalki/powersensitivities.jl
true
true
true
none
https://paperswithcode.com/paper/superbench-a-super-resolution-benchmark
SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning
2306.14070
https://arxiv.org/abs/2306.14070v2
https://arxiv.org/pdf/2306.14070v2.pdf
https://github.com/erichson/superbench
true
true
false
pytorch
https://paperswithcode.com/paper/sups-a-simulated-underground-parking-scenario
SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous Driving
2302.12966
https://arxiv.org/abs/2302.12966v1
https://arxiv.org/pdf/2302.12966v1.pdf
https://github.com/jarvishou829/sups
true
true
true
pytorch
https://paperswithcode.com/paper/squeezenet-alexnet-level-accuracy-with-50x
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
1602.07360
http://arxiv.org/abs/1602.07360v4
http://arxiv.org/pdf/1602.07360v4.pdf
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/squeezenet
false
false
false
mindspore
https://paperswithcode.com/paper/shapley-based-residual-decomposition-for
Shapley Based Residual Decomposition for Instance Analysis
2305.18818
https://arxiv.org/abs/2305.18818v1
https://arxiv.org/pdf/2305.18818v1.pdf
https://github.com/uilymmot/residual-decomposition
true
true
false
none
https://paperswithcode.com/paper/multi-view-transformer-for-3d-visual
Multi-View Transformer for 3D Visual Grounding
2204.02174
https://arxiv.org/abs/2204.02174v1
https://arxiv.org/pdf/2204.02174v1.pdf
https://github.com/sega-hsj/mvt-3dvg
true
true
true
pytorch
https://paperswithcode.com/paper/generating-molecular-fragmentation-graphs
Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks
2304.13136
https://arxiv.org/abs/2304.13136v2
https://arxiv.org/pdf/2304.13136v2.pdf
https://github.com/samgoldman97/ms-pred
true
true
true
pytorch
https://paperswithcode.com/paper/hybrid-ensemble-for-fake-news-detection-an
Hybrid Ensemble for Fake News Detection: An attempt
2206.13981
https://arxiv.org/abs/2206.13981v1
https://arxiv.org/pdf/2206.13981v1.pdf
https://github.com/singh-l/hybrrid_fn_dat_
true
true
false
none
https://paperswithcode.com/paper/resurrecting-recurrent-neural-networks-for
Resurrecting Recurrent Neural Networks for Long Sequences
2303.06349
https://arxiv.org/abs/2303.06349v1
https://arxiv.org/pdf/2303.06349v1.pdf
https://github.com/LuCeHe/lru_unofficial
false
false
false
tf
https://paperswithcode.com/paper/augmenting-photometric-redshift-estimates
Augmenting photometric redshift estimates using spectroscopic nearest neighbours
2211.01901
https://arxiv.org/abs/2211.01901v2
https://arxiv.org/pdf/2211.01901v2.pdf
https://github.com/tos-1/neznet
true
true
true
tf
https://paperswithcode.com/paper/machine-learning-featurizations-for-ai
Machine Learning Featurizations for AI Hacking of Political Systems
2110.09231
https://arxiv.org/abs/2110.09231v2
https://arxiv.org/pdf/2110.09231v2.pdf
https://github.com/nesanders/ai_hacking_featurization
true
false
false
none
https://paperswithcode.com/paper/what-can-we-learn-from-collective-human
What Can We Learn from Collective Human Opinions on Natural Language Inference Data?
2010.03532
https://arxiv.org/abs/2010.03532v2
https://arxiv.org/pdf/2010.03532v2.pdf
https://github.com/easonnie/ChaosNLI
true
true
true
pytorch
https://paperswithcode.com/paper/simple-adaptive-projection-with-pretrained
Constrained Adaptive Projection with Pretrained Features for Anomaly Detection
2112.02597
https://arxiv.org/abs/2112.02597v2
https://arxiv.org/pdf/2112.02597v2.pdf
https://github.com/tabguigui/cap
true
true
true
pytorch
https://paperswithcode.com/paper/distributed-nli-learning-to-predict-human
Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning
2104.08676
https://arxiv.org/abs/2104.08676v2
https://arxiv.org/pdf/2104.08676v2.pdf
https://github.com/easonnie/ChaosNLI
true
true
true
pytorch
https://paperswithcode.com/paper/what-you-see-is-what-you-get-distributional
What You See is What You Get: Principled Deep Learning via Distributional Generalization
2204.03230
https://arxiv.org/abs/2204.03230v2
https://arxiv.org/pdf/2204.03230v2.pdf
https://github.com/yangarbiter/dp-dg
true
true
true
pytorch
https://paperswithcode.com/paper/regularized-deep-learning-with-a-non-convex
Regularized deep learning with nonconvex penalties
1909.05142
https://arxiv.org/abs/1909.05142v4
https://arxiv.org/pdf/1909.05142v4.pdf
https://github.com/mjohn5/dnn_laplace_arctan_regularization
false
false
true
none
https://paperswithcode.com/paper/a-dual-semantic-aware-recurrent-global
A Dual Semantic-Aware Recurrent Global-Adaptive Network For Vision-and-Language Navigation
2305.03602
https://arxiv.org/abs/2305.03602v2
https://arxiv.org/pdf/2305.03602v2.pdf
https://github.com/crystalsixone/dsrg
true
true
false
pytorch
https://paperswithcode.com/paper/rocket-exceptionally-fast-and-accurate-time
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels
1910.13051
https://arxiv.org/abs/1910.13051v1
https://arxiv.org/pdf/1910.13051v1.pdf
https://github.com/angus924/rocket
true
true
true
pytorch
https://paperswithcode.com/paper/speechmoe-scaling-to-large-acoustic-models
SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts
2105.03036
https://arxiv.org/abs/2105.03036v1
https://arxiv.org/pdf/2105.03036v1.pdf
https://github.com/tencent-ailab/3m-asr
false
false
true
pytorch
https://paperswithcode.com/paper/first-the-worst-finding-better-gender
First the worst: Finding better gender translations during beam search
2104.07429
https://arxiv.org/abs/2104.07429v2
https://arxiv.org/pdf/2104.07429v2.pdf
https://github.com/dcsaunders/nmt-gender-rerank
true
true
true
none
https://paperswithcode.com/paper/3m-multi-loss-multi-path-and-multi-level
3M: Multi-loss, Multi-path and Multi-level Neural Networks for speech recognition
2204.03178
https://arxiv.org/abs/2204.03178v2
https://arxiv.org/pdf/2204.03178v2.pdf
https://github.com/tencent-ailab/3m-asr
true
true
true
pytorch
https://paperswithcode.com/paper/cohort-state-transition-models-in-r-from
An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example
2001.07824
https://arxiv.org/abs/2001.07824v4
https://arxiv.org/pdf/2001.07824v4.pdf
https://github.com/DARTH-git/cohort-modeling-tutorial-timedep
false
false
true
none
https://paperswithcode.com/paper/parameter-estimation-with-gravitational-waves
Parameter estimation with gravitational waves
2204.04449
https://arxiv.org/abs/2204.04449v1
https://arxiv.org/pdf/2204.04449v1.pdf
https://github.com/oshaughn/research-projects-rit
true
true
false
none
https://paperswithcode.com/paper/provable-defense-against-privacy-leakage-in
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective
2012.06043
https://arxiv.org/abs/2012.06043v1
https://arxiv.org/pdf/2012.06043v1.pdf
https://github.com/eth-sri/bayes-framework-leakage
false
false
true
jax
https://paperswithcode.com/paper/accurate-clinical-toxicity-prediction-using
Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets and Contrastive Molecular Explanations
2204.06614
https://arxiv.org/abs/2204.06614v1
https://arxiv.org/pdf/2204.06614v1.pdf
https://github.com/IBM/Contrastive-Explanation-Method
true
true
false
tf
https://paperswithcode.com/paper/an-image-is-worth-16x16-words-transformers-1
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
2010.11929
https://arxiv.org/abs/2010.11929v2
https://arxiv.org/pdf/2010.11929v2.pdf
https://github.com/uzi0espil/research-papers-implementation/tree/master/Vision%20Transformer
false
false
false
tf
https://paperswithcode.com/paper/efficient-estimation-of-pairwise-effective
Efficient Estimation of Pairwise Effective Resistance
2312.06123
https://arxiv.org/abs/2312.06123v1
https://arxiv.org/pdf/2312.06123v1.pdf
https://github.com/anryyang/geer
true
true
false
none
https://paperswithcode.com/paper/fine-tuning-discrete-diffusion-models-via
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design
2410.13643
https://arxiv.org/abs/2410.13643v1
https://arxiv.org/pdf/2410.13643v1.pdf
https://github.com/chenyuwang-monica/drakes
true
true
true
pytorch
https://paperswithcode.com/paper/comprehending-and-ordering-semantics-for-1
Comprehending and Ordering Semantics for Image Captioning
2206.06930
https://arxiv.org/abs/2206.06930v1
https://arxiv.org/pdf/2206.06930v1.pdf
https://github.com/yehli/xmodaler
true
true
false
pytorch
https://paperswithcode.com/paper/rejuvenating-low-frequency-words-making-the
Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation
2106.00903
https://arxiv.org/abs/2106.00903v2
https://arxiv.org/pdf/2106.00903v2.pdf
https://github.com/alphadl/rlfw-nat
true
true
false
pytorch
https://paperswithcode.com/paper/pseudo-numerical-methods-for-diffusion-models-1
Pseudo Numerical Methods for Diffusion Models on Manifolds
2202.09778
https://arxiv.org/abs/2202.09778v2
https://arxiv.org/pdf/2202.09778v2.pdf
https://github.com/compvis/latent-diffusion
false
false
true
pytorch
https://paperswithcode.com/paper/behrt-transformer-for-electronic-health
BEHRT: Transformer for Electronic Health Records
1907.09538
https://arxiv.org/abs/1907.09538v1
https://arxiv.org/pdf/1907.09538v1.pdf
https://github.com/yikuanli/BEHRT
true
false
false
pytorch
https://paperswithcode.com/paper/parallelization-of-the-symplectic-massive
Parallelization of the Symplectic Massive Body Algorithm (SyMBA) $N$-body Code
2304.07325
https://arxiv.org/abs/2304.07325v1
https://arxiv.org/pdf/2304.07325v1.pdf
https://github.com/tommylauch/swift_symbap_pub
true
true
false
none
https://paperswithcode.com/paper/specnet2-orthogonalization-free-spectral
SpecNet2: Orthogonalization-free spectral embedding by neural networks
2206.06644
https://arxiv.org/abs/2206.06644v1
https://arxiv.org/pdf/2206.06644v1.pdf
https://github.com/ziyuchen7/specnet2
true
true
false
none
https://paperswithcode.com/paper/detectordetective-investigating-the-effects
DetectorDetective: Investigating the Effects of Adversarial Examples on Object Detectors
null
http://openaccess.thecvf.com//content/CVPR2022/html/Vellaichamy_DetectorDetective_Investigating_the_Effects_of_Adversarial_Examples_on_Object_Detectors_CVPR_2022_paper.html
http://openaccess.thecvf.com//content/CVPR2022/papers/Vellaichamy_DetectorDetective_Investigating_the_Effects_of_Adversarial_Examples_on_Object_Detectors_CVPR_2022_paper.pdf
https://github.com/poloclub/detector-detective
true
true
false
none
https://paperswithcode.com/paper/dynapicker-dynamic-convolutional-neural
Real-time Earthquake Monitoring using Deep Learning: a case study on Turkey Earthquake Aftershock Sequence
2211.09539
https://arxiv.org/abs/2211.09539v2
https://arxiv.org/pdf/2211.09539v2.pdf
https://github.com/srivastavaresearchgroup/saipy
false
false
true
pytorch
https://paperswithcode.com/paper/a-novel-facial-emotion-recognition-model
A novel facial emotion recognition model using segmentation VGG-19 architecture
null
https://link.springer.com/article/10.1007/s41870-023-01184-z
https://link.springer.com/article/10.1007/s41870-023-01184-z
https://github.com/VigneshS10/Segmentation-VGG19
true
false
false
pytorch
https://paperswithcode.com/paper/the-universality-in-urban-commuting-across
The universality in urban commuting across and within cities
2204.12865
https://arxiv.org/abs/2204.12865v1
https://arxiv.org/pdf/2204.12865v1.pdf
https://github.com/leiii/commute
true
true
false
none
https://paperswithcode.com/paper/masked-spectrogram-prediction-for-self
Masked Spectrogram Prediction For Self-Supervised Audio Pre-Training
2204.12768
https://arxiv.org/abs/2204.12768v1
https://arxiv.org/pdf/2204.12768v1.pdf
https://github.com/wanghelin1997/maskspec
true
true
true
pytorch
https://paperswithcode.com/paper/prompt-tuning-for-discriminative-pre-trained-1
Prompt Tuning for Discriminative Pre-trained Language Models
2205.11166
https://arxiv.org/abs/2205.11166v1
https://arxiv.org/pdf/2205.11166v1.pdf
https://github.com/thunlp/dpt
true
true
false
pytorch
https://paperswithcode.com/paper/i-learn-to-diffuse-or-data-alchemy-101-a
I Learn to Diffuse, or Data Alchemy 101: a Mnemonic Manifesto
2208.03998
https://arxiv.org/abs/2208.03998v2
https://arxiv.org/pdf/2208.03998v2.pdf
https://github.com/alembics/disco-diffusion
true
true
false
pytorch
https://paperswithcode.com/paper/occlusion-robust-face-alignment-using-a
Occlusion-Robust Face Alignment Using a Viewpoint-Invariant Hierarchical Network Architecture
null
http://openaccess.thecvf.com//content/CVPR2022/html/Zhu_Occlusion-Robust_Face_Alignment_Using_a_Viewpoint-Invariant_Hierarchical_Network_Architecture_CVPR_2022_paper.html
http://openaccess.thecvf.com//content/CVPR2022/papers/Zhu_Occlusion-Robust_Face_Alignment_Using_a_Viewpoint-Invariant_Hierarchical_Network_Architecture_CVPR_2022_paper.pdf
https://github.com/zhuccly/glomface-face-alignment
true
true
false
tf
https://paperswithcode.com/paper/show-deconfound-and-tell-image-captioning
Show, Deconfound and Tell: Image Captioning With Causal Inference
null
http://openaccess.thecvf.com//content/CVPR2022/html/Liu_Show_Deconfound_and_Tell_Image_Captioning_With_Causal_Inference_CVPR_2022_paper.html
http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_Show_Deconfound_and_Tell_Image_Captioning_With_Causal_Inference_CVPR_2022_paper.pdf
https://github.com/cumtgg/ciic
true
true
false
pytorch
https://paperswithcode.com/paper/hafix-history-augmented-large-language-models
HAFix: History-Augmented Large Language Models for Bug Fixing
2501.09135
https://arxiv.org/abs/2501.09135v1
https://arxiv.org/pdf/2501.09135v1.pdf
https://github.com/sailresearch/hafix-history-augmented-llms-for-bug-fixing
true
true
false
none