Split (1)

train · 300k rows

paper_url stringlengths 36 81	paper_title stringlengths 1 242 ⌀	paper_arxiv_id stringlengths 9 16 ⌀	paper_url_abs stringlengths 18 314	paper_url_pdf stringlengths 21 935 ⌀	repo_url stringlengths 26 200	is_official bool 2 classes	mentioned_in_paper bool 2 classes	mentioned_in_github bool 2 classes	framework stringclasses 9 values
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

Subsets and Splits

Framework Repo Connectivity Analysis

Reveals the number of official and unofficial repositories and papers associated with different frameworks, highlighting the most connected ones.

Deduplicated Paper-Code Links

This query provides a detailed and organized list of repositories linked to single papers, highlighting official status and mention sources, which is useful for understanding the relationship between papers and their corresponding repositories.

Paper Repo Counts & Distribution

Provides detailed statistics on the distribution of papers across different numbers of repositories, highlighting the percentage of papers with multiple repositories.

Quantum Papers with Code Links

Lists quantum-related papers with their titles, arXiv IDs, frameworks, and code repository links, providing a valuable resource for researchers interested in quantum computing.

Financial Stock Price Prediction

Finds papers related to stock prices, financial markets, and predictions, providing a focused subset for further analysis.

SQL Console for pwc-archive/links-between-paper-and-code

Retrieves specific details about a single paper by its arXiv ID, providing limited insight into the dataset.

Search for YOLO Links

Retrieves a limited set of records related to YOLO, providing basic information about papers and repositories but without deeper analysis.

Prompt Optimization and Personalization

Retrieves a limited set of papers with titles containing specific keywords related to prompt optimization and personalization, providing basic filtering of the dataset.