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/do-transformers-really-perform-bad-for-graph	Do Transformers Really Perform Bad for Graph Representation?	2106.05234	https://arxiv.org/abs/2106.05234v5	https://arxiv.org/pdf/2106.05234v5.pdf	https://github.com/microsoft/Graphormer	false	false	false	pytorch
https://paperswithcode.com/paper/stagewise-unsupervised-domain-adaptation-with	Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images	2108.12611	https://arxiv.org/abs/2108.12611v1	https://arxiv.org/pdf/2108.12611v1.pdf	https://github.com/lanmng/roadda	true	true	false	pytorch
https://paperswithcode.com/paper/multi-objective-conflict-based-search-for	A Conflict-Based Search Framework for Multi-Objective Multi-Agent Path Finding	2101.03805	https://arxiv.org/abs/2101.03805v5	https://arxiv.org/pdf/2101.03805v5.pdf	https://github.com/wonderren/public_cppmomapf	true	true	true	none
https://paperswithcode.com/paper/subdimensional-expansion-for-multi-objective	Subdimensional Expansion for Multi-objective Multi-agent Path Finding	2102.01353	https://arxiv.org/abs/2102.01353v2	https://arxiv.org/pdf/2102.01353v2.pdf	https://github.com/wonderren/public_cppmomapf	false	false	true	none
https://paperswithcode.com/paper/conceptual-compression-via-deep-structure-and	Conceptual Compression via Deep Structure and Texture Synthesis	2011.04976	https://arxiv.org/abs/2011.04976v2	https://arxiv.org/pdf/2011.04976v2.pdf	https://github.com/changjianhui/LCIC-pytorch	true	false	true	pytorch
https://paperswithcode.com/paper/single-stream-cnn-with-learnable-architecture	Single-stream CNN with Learnable Architecture for Multi-source Remote Sensing Data	2109.06094	https://arxiv.org/abs/2109.06094v2	https://arxiv.org/pdf/2109.06094v2.pdf	https://github.com/yyyyangyi/multi-source-rs-dgconv	true	true	true	pytorch
https://paperswithcode.com/paper/awardee-solution-of-kdd-cup-2021-ogb-large	First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track	2106.08279	https://arxiv.org/abs/2106.08279v3	https://arxiv.org/pdf/2106.08279v3.pdf	https://github.com/microsoft/Graphormer	false	false	true	pytorch
https://paperswithcode.com/paper/multi-objective-conflict-based-search-using	Multi-objective Conflict-based Search Using Safe-interval Path Planning	2108.00745	https://arxiv.org/abs/2108.00745v3	https://arxiv.org/pdf/2108.00745v3.pdf	https://github.com/wonderren/public_cppmomapf	false	false	true	none
https://paperswithcode.com/paper/an-integrated-auto-encoder-block-switching-1	An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks	2203.10930	https://arxiv.org/abs/2203.10930v1	https://arxiv.org/pdf/2203.10930v1.pdf	https://github.com/anirudh9784/Adversarial-Attacks-and-Defences	true	false	false	pytorch
https://paperswithcode.com/paper/noise-dynamics-of-quantum-annealers	Noise Dynamics of Quantum Annealers: Estimating the Effective Noise Using Idle Qubits	2209.05648	https://arxiv.org/abs/2209.05648v2	https://arxiv.org/pdf/2209.05648v2.pdf	https://github.com/lanl/noise-indicator-qa	true	true	false	none
https://paperswithcode.com/paper/on-the-performance-of-deep-learning-models	On the performance of deep learning models for time series classification in streaming	2003.02544	https://arxiv.org/abs/2003.02544v2	https://arxiv.org/pdf/2003.02544v2.pdf	https://github.com/pedrolarben/ADLStream	true	true	true	tf
https://paperswithcode.com/paper/ktrl-f-knowledge-augmented-in-document-search	KTRL+F: Knowledge-Augmented In-Document Search	2311.08329	https://arxiv.org/abs/2311.08329v4	https://arxiv.org/pdf/2311.08329v4.pdf	https://github.com/kaistai/ktrlf	true	true	true	none
https://paperswithcode.com/paper/depts-deep-expansion-learning-for-periodic-1	DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting	2203.07681	https://arxiv.org/abs/2203.07681v1	https://arxiv.org/pdf/2203.07681v1.pdf	https://github.com/weifantt/depts	true	true	false	pytorch
https://paperswithcode.com/paper/n-beats-neural-basis-expansion-analysis-for	N-BEATS: Neural basis expansion analysis for interpretable time series forecasting	1905.10437	https://arxiv.org/abs/1905.10437v4	https://arxiv.org/pdf/1905.10437v4.pdf	https://github.com/weifantt/depts	false	false	true	pytorch
https://paperswithcode.com/paper/progressive-end-to-end-object-detection-in	Progressive End-to-End Object Detection in Crowded Scenes	2203.07669	https://arxiv.org/abs/2203.07669v3	https://arxiv.org/pdf/2203.07669v3.pdf	https://github.com/megvii-model/iter-e2edet	true	true	false	pytorch
https://paperswithcode.com/paper/transfer-learning-with-gaussian-processes-for	Transfer Learning with Gaussian Processes for Bayesian Optimization	2111.11223	https://arxiv.org/abs/2111.11223v2	https://arxiv.org/pdf/2111.11223v2.pdf	https://github.com/boschresearch/transfergpbo	true	true	true	none
https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning	1602.07261	http://arxiv.org/abs/1602.07261v2	http://arxiv.org/pdf/1602.07261v2.pdf	https://github.com/2023-MindSpore-1/ms-code-33	false	false	false	mindspore
https://paperswithcode.com/paper/inceptionnext-when-inception-meets-convnext	InceptionNeXt: When Inception Meets ConvNeXt	2303.16900	https://arxiv.org/abs/2303.16900v2	https://arxiv.org/pdf/2303.16900v2.pdf	https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/inceptionnext	false	false	false	tf
https://paperswithcode.com/paper/querying-inconsistent-prioritized-data-with	Querying Inconsistent Prioritized Data with ORBITS: Algorithms, Implementation, and Experiments	2202.07980	https://arxiv.org/abs/2202.07980v2	https://arxiv.org/pdf/2202.07980v2.pdf	https://github.com/bourgaux/orbits	true	true	true	none
https://paperswithcode.com/paper/diagnosis-of-covid-19-using-chest-x-ray	Diagnosis of COVID-19 using chest X-ray images based on modified DarkCovidNet model	null	https://link.springer.com/article/10.1007/s12065-021-00679-7	https://link.springer.com/content/pdf/10.1007/s12065-021-00679-7.pdf	https://github.com/Dawit1922/Modified-DarkCovidNet	false	false	false	none
https://paperswithcode.com/paper/global-filter-networks-for-image	Global Filter Networks for Image Classification	2107.00645	https://arxiv.org/abs/2107.00645v2	https://arxiv.org/pdf/2107.00645v2.pdf	https://github.com/dslisleedh/MLP_based_models-tensorflow2/blob/master/gfnet.py	false	false	false	tf
https://paperswithcode.com/paper/ufal-corpipe-at-crac-2022-effectivity-of	ÚFAL CorPipe at CRAC 2022: Effectivity of Multilingual Models for Coreference Resolution	2209.07278	https://arxiv.org/abs/2209.07278v3	https://arxiv.org/pdf/2209.07278v3.pdf	https://github.com/ufal/crac2022-corpipe	true	true	true	tf
https://paperswithcode.com/paper/certrl-formalizing-convergence-proofs-for	CertRL: Formalizing Convergence Proofs for Value and Policy Iteration in Coq	2009.11403	https://arxiv.org/abs/2009.11403v2	https://arxiv.org/pdf/2009.11403v2.pdf	https://github.com/IBM/FormalML	true	false	true	none
https://paperswithcode.com/paper/stochastic-subgradient-method-converges-on	Stochastic subgradient method converges on tame functions	1804.07795	http://arxiv.org/abs/1804.07795v3	http://arxiv.org/pdf/1804.07795v3.pdf	https://github.com/IBM/FormalML	false	false	true	none
https://paperswithcode.com/paper/explaining-and-harnessing-adversarial	Explaining and Harnessing Adversarial Examples	1412.6572	http://arxiv.org/abs/1412.6572v3	http://arxiv.org/pdf/1412.6572v3.pdf	https://github.com/anirudh9784/Adversarial-Attacks-and-Defences	false	false	true	pytorch
https://paperswithcode.com/paper/formalization-of-a-stochastic-approximation	Formalization of a Stochastic Approximation Theorem	2202.05959	https://arxiv.org/abs/2202.05959v2	https://arxiv.org/pdf/2202.05959v2.pdf	https://github.com/IBM/FormalML	true	true	true	none
https://paperswithcode.com/paper/ssd-single-shot-multibox-detector	SSD: Single Shot MultiBox Detector	1512.02325	http://arxiv.org/abs/1512.02325v5	http://arxiv.org/pdf/1512.02325v5.pdf	https://github.com/manishsoni1908/mobilenet-ssd-keras	false	false	true	tf
https://paperswithcode.com/paper/tgl-a-general-framework-for-temporal-gnn	TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs	2203.14883	https://arxiv.org/abs/2203.14883v2	https://arxiv.org/pdf/2203.14883v2.pdf	https://github.com/amazon-research/tgl	true	true	false	pytorch
https://paperswithcode.com/paper/error-profile-for-discontinuous-galerkin-time	Error Profile for Discontinuous Galerkin Time Stepping of Parabolic PDEs	2208.03846	https://arxiv.org/abs/2208.03846v2	https://arxiv.org/pdf/2208.03846v2.pdf	https://github.com/billmclean/dgerrorprofile	true	true	true	none
https://paperswithcode.com/paper/reinforcement-learned-distributed-multi-robot	Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewards	2203.10229	https://arxiv.org/abs/2203.10229v1	https://arxiv.org/pdf/2203.10229v1.pdf	https://github.com/hanruihua/rl_rvo_nav	true	true	true	pytorch
https://paperswithcode.com/paper/metaphors-in-pre-trained-language-models	Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages	2203.14139	https://arxiv.org/abs/2203.14139v1	https://arxiv.org/pdf/2203.14139v1.pdf	https://github.com/ehsanaghazadeh/metaphors_in_plms	true	true	true	pytorch
https://paperswithcode.com/paper/freggan-with-k-space-loss-regularization-for	fRegGAN with K-space Loss Regularization for Medical Image Translation	2303.15938	https://arxiv.org/abs/2303.15938v2	https://arxiv.org/pdf/2303.15938v2.pdf	https://github.com/bayer-group/freggan	true	true	true	pytorch
https://paperswithcode.com/paper/sequential-predictive-conformal-inference-for	Sequential Predictive Conformal Inference for Time Series	2212.03463	https://arxiv.org/abs/2212.03463v3	https://arxiv.org/pdf/2212.03463v3.pdf	https://github.com/hamrel-cxu/spci-code	true	true	false	pytorch
https://paperswithcode.com/paper/moving-obstacle-avoidance-a-data-driven-risk	Moving Obstacle Avoidance: a Data-Driven Risk-Aware Approach	2203.14913	https://arxiv.org/abs/2203.14913v1	https://arxiv.org/pdf/2203.14913v1.pdf	https://github.com/skylarxwei/riskaware_mpc_ssa_sim	true	true	false	none
https://paperswithcode.com/paper/vakyansh-asr-toolkit-for-low-resource-indic	Vakyansh: ASR Toolkit for Low Resource Indic languages	2203.16512	https://arxiv.org/abs/2203.16512v2	https://arxiv.org/pdf/2203.16512v2.pdf	https://github.com/Open-Speech-EkStep/vakyansh-models	true	true	true	none
https://paperswithcode.com/paper/forecast-mae-self-supervised-pre-training-for	Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders	2308.09882	https://arxiv.org/abs/2308.09882v1	https://arxiv.org/pdf/2308.09882v1.pdf	https://github.com/jchengai/forecast-mae	true	true	true	pytorch
https://paperswithcode.com/paper/graph-neural-networks-in-iot-a-survey	Graph Neural Networks in IoT: A Survey	2203.15935	https://arxiv.org/abs/2203.15935v2	https://arxiv.org/pdf/2203.15935v2.pdf	https://github.com/guimindong/gnn4iot	true	true	true	none
https://paperswithcode.com/paper/a-fast-algorithm-for-convolutional-structured	A Fast Algorithm for Convolutional Structured Low-Rank Matrix Recovery	1609.07429	https://arxiv.org/abs/1609.07429v3	https://arxiv.org/pdf/1609.07429v3.pdf	https://github.com/yhao-z/GIRAF-3d-CG	false	false	false	none
https://paperswithcode.com/paper/large-language-models-are-state-of-the-art-1	ICE-Score: Instructing Large Language Models to Evaluate Code	2304.14317	https://arxiv.org/abs/2304.14317v2	https://arxiv.org/pdf/2304.14317v2.pdf	https://github.com/terryyz/llm-code-eval	true	true	true	none
https://paperswithcode.com/paper/mixing-dirichlet-topic-models-and-word	Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec	1605.02019	http://arxiv.org/abs/1605.02019v1	http://arxiv.org/pdf/1605.02019v1.pdf	https://github.com/Wurmloch/TopicModeling	false	false	true	none
https://paperswithcode.com/paper/discovering-new-intents-with-deep-aligned	Discovering New Intents with Deep Aligned Clustering	2012.08987	https://arxiv.org/abs/2012.08987v7	https://arxiv.org/pdf/2012.08987v7.pdf	https://github.com/hanleizhang/DeepAligned-Clustering	true	true	true	pytorch
https://paperswithcode.com/paper/efficient-data-compression-for-3d-sparse-tpc	Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder	2111.05423	https://arxiv.org/abs/2111.05423v1	https://arxiv.org/pdf/2111.05423v1.pdf	https://github.com/BNL-DAQ-LDRD/NeuralCompression	true	false	false	pytorch
https://paperswithcode.com/paper/accelerating-the-super-resolution	Accelerating the Super-Resolution Convolutional Neural Network	1608.00367	http://arxiv.org/abs/1608.00367v1	http://arxiv.org/pdf/1608.00367v1.pdf	https://github.com/Nhat-Thanh/FSRCNN-Pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/towards-fairness-aware-adversarial-learning	Towards Fairness-Aware Adversarial Learning	2402.17729	https://arxiv.org/abs/2402.17729v2	https://arxiv.org/pdf/2402.17729v2.pdf	https://github.com/TrustAI/FAAL	true	true	false	pytorch
https://paperswithcode.com/paper/rcabench-open-benchmarking-platform-for-root	RCABench: Open Benchmarking Platform for Root Cause Analysis	2303.05029	https://arxiv.org/abs/2303.05029v2	https://arxiv.org/pdf/2303.05029v2.pdf	https://github.com/ricseclab/rcabench	true	true	false	none
https://paperswithcode.com/paper/cicero-a-dataset-for-contextualized	CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues	2203.13926	https://arxiv.org/abs/2203.13926v3	https://arxiv.org/pdf/2203.13926v3.pdf	https://github.com/declare-lab/CICERO	true	false	true	none
https://paperswithcode.com/paper/chordal-sparsity-for-lipschitz-constant	Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks	2204.00846	https://arxiv.org/abs/2204.00846v2	https://arxiv.org/pdf/2204.00846v2.pdf	https://github.com/antonxue/chordal-lipsdp	true	true	false	none
https://paperswithcode.com/paper/formalization-of-dependent-type-theory-the	Formalization of dependent type theory: The example of CaTT	2111.14736	https://arxiv.org/abs/2111.14736v1	https://arxiv.org/pdf/2111.14736v1.pdf	https://github.com/thibautbenjamin/catt-formalization	true	true	true	none
https://paperswithcode.com/paper/bimodal-distributed-binarized-neural-networks	Bimodal Distributed Binarized Neural Networks	2204.02004	https://arxiv.org/abs/2204.02004v1	https://arxiv.org/pdf/2204.02004v1.pdf	https://github.com/blueanon/bd-bnn	true	true	true	pytorch
https://paperswithcode.com/paper/universalner-targeted-distillation-from-large	UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition	2308.03279	https://arxiv.org/abs/2308.03279v2	https://arxiv.org/pdf/2308.03279v2.pdf	https://github.com/universal-ner/universal-ner	false	false	true	pytorch
https://paperswithcode.com/paper/name-your-style-an-arbitrary-artist-aware	Name Your Style: An Arbitrary Artist-aware Image Style Transfer	2202.13562	https://arxiv.org/abs/2202.13562v3	https://arxiv.org/pdf/2202.13562v3.pdf	https://github.com/Holmes-Alan/TxST	true	false	true	pytorch
https://paperswithcode.com/paper/cycle-index-polynomials-and-generalized	Cycle Index Polynomials and Generalized Quantum Separability Tests	2208.14596	https://arxiv.org/abs/2208.14596v3	https://arxiv.org/pdf/2208.14596v3.pdf	https://github.com/mlabo15/GeneralizedSeparability	true	true	false	none
https://paperswithcode.com/paper/nevis-22-a-stream-of-100-tasks-sampled-from	NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research	2211.11747	https://arxiv.org/abs/2211.11747v2	https://arxiv.org/pdf/2211.11747v2.pdf	https://github.com/deepmind/dm_nevis	true	true	true	jax
https://paperswithcode.com/paper/multi-class-probabilistic-classification	Multi-class probabilistic classification using inductive and cross Venn-Abers predictors	null	https://proceedings.mlr.press/v60/manokhin17a.html	https://proceedings.mlr.press/v60/manokhin17a.html	https://github.com/valeman/Multi-class-probabilistic-classification	true	false	false	none
https://paperswithcode.com/paper/deformable-model-driven-neural-rendering-for	Deformable Model-Driven Neural Rendering for High-Fidelity 3D Reconstruction of Human Heads Under Low-View Settings	2303.13855	https://arxiv.org/abs/2303.13855v2	https://arxiv.org/pdf/2303.13855v2.pdf	https://github.com/xubaixinxbx/3dheads	true	true	true	pytorch
https://paperswithcode.com/paper/stabilization-of-affine-systems-with	Stabilization of affine systems with polytopic control value sets	2112.02451	https://arxiv.org/abs/2112.02451v2	https://arxiv.org/pdf/2112.02451v2.pdf	https://github.com/Stabilization-over-polytopic-CVS/Stabilization-of-affine-systems-with-polytopic-control-value-sets	true	false	false	none
https://paperswithcode.com/paper/thermodynamical-material-networks-for	Thermodynamical Material Networks for Modeling, Planning, and Control of Circular Material Flows	2111.10693	https://arxiv.org/abs/2111.10693v3	https://arxiv.org/pdf/2111.10693v3.pdf	https://github.com/fedezocco/tmnbiometh-scipy	true	true	false	none
https://paperswithcode.com/paper/deep-neural-network-representation-of-density	Deep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation	2104.03786	https://arxiv.org/abs/2104.03786v2	https://arxiv.org/pdf/2104.03786v2.pdf	https://github.com/mzjb/deeph-pack	true	true	true	pytorch
https://paperswithcode.com/paper/ehrcon-dataset-for-checking-consistency	EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records	2406.16341	https://arxiv.org/abs/2406.16341v2	https://arxiv.org/pdf/2406.16341v2.pdf	https://github.com/dustn1259/ehrcon	true	true	true	pytorch
https://paperswithcode.com/paper/a-novel-bayesian-extrapolation-design-for	A Novel Bayesian Extrapolation Design for Assessing Equivalence in Exposure-Response Curves between Pediatric and Adult Populations	2505.17397	https://arxiv.org/abs/2505.17397v1	https://arxiv.org/pdf/2505.17397v1.pdf	https://github.com/zhongheng-Biostatistics/Pediatric-Bayesian-Extrapolation-Design	true	false	false	none
https://paperswithcode.com/paper/ecg-arrhythmia-classification-using-a-2-d	ECG arrhythmia classification using a 2-D convolutional neural network	1804.06812	http://arxiv.org/abs/1804.06812v1	http://arxiv.org/pdf/1804.06812v1.pdf	https://github.com/celiedel/ECG_Classification_with_2D_CNN	false	false	true	none
https://paperswithcode.com/paper/devil-is-in-channels-contrastive-single	Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation	2306.05254	https://arxiv.org/abs/2306.05254v2	https://arxiv.org/pdf/2306.05254v2.pdf	https://github.com/shishuaihu/ccsdg	true	true	true	pytorch
https://paperswithcode.com/paper/automatic-classification-of-stop-realisation	Automatic classification of stop realisation with wav2vec2.0	2505.23688	https://arxiv.org/abs/2505.23688v2	https://arxiv.org/pdf/2505.23688v2.pdf	https://github.com/james-tanner/wav2vec-burst-detection	true	false	false	pytorch
https://paperswithcode.com/paper/190412593	Density-based Community Detection/Optimization	1904.12593	http://arxiv.org/abs/1904.12593v1	http://arxiv.org/pdf/1904.12593v1.pdf	https://github.com/cran/DynComm	false	false	true	none
https://paperswithcode.com/paper/unsupervised-representation-learning-with-1	Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks	1511.06434	http://arxiv.org/abs/1511.06434v2	http://arxiv.org/pdf/1511.06434v2.pdf	https://github.com/amaranth819/dcgan-cifar10-pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/local-sample-weighted-multiple-kernel	Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph	2207.02846	https://arxiv.org/abs/2207.02846v1	https://arxiv.org/pdf/2207.02846v1.pdf	https://github.com/liliangnudt/lswmkc	true	true	false	none
https://paperswithcode.com/paper/investigating-accuracy-novelty-performance	Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering	2204.12326	https://arxiv.org/abs/2204.12326v2	https://arxiv.org/pdf/2204.12326v2.pdf	https://github.com/fuxiailab/r-adjnorm	true	true	false	tf
https://paperswithcode.com/paper/low-light-maritime-image-enhancement-with	Low-Light Maritime Image Enhancement with Regularized Illumination Optimization and Deep Noise Suppression	2008.03765	https://arxiv.org/abs/2008.03765v1	https://arxiv.org/pdf/2008.03765v1.pdf	https://github.com/gy65896/Enhancement-Access	false	false	false	none
https://paperswithcode.com/paper/simcse-simple-contrastive-learning-of	SimCSE: Simple Contrastive Learning of Sentence Embeddings	2104.08821	https://arxiv.org/abs/2104.08821v4	https://arxiv.org/pdf/2104.08821v4.pdf	https://github.com/dltmddbs100/SimCSE	false	false	true	pytorch
https://paperswithcode.com/paper/crackseg9k-a-collection-and-benchmark-for	CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks	2208.13054	https://arxiv.org/abs/2208.13054v1	https://arxiv.org/pdf/2208.13054v1.pdf	https://github.com/Dhananjay42/crackseg9k	true	false	true	pytorch
https://paperswithcode.com/paper/easy-and-efficient-transformer-scalable	Easy and Efficient Transformer : Scalable Inference Solution For large NLP model	2104.12470	https://arxiv.org/abs/2104.12470v5	https://arxiv.org/pdf/2104.12470v5.pdf	https://github.com/NetEase-FuXi/EET	true	true	true	pytorch
https://paperswithcode.com/paper/controllable-textual-inversion-for	Controllable Textual Inversion for Personalized Text-to-Image Generation	2304.05265	https://arxiv.org/abs/2304.05265v3	https://arxiv.org/pdf/2304.05265v3.pdf	https://github.com/jnzju/coti	true	true	false	pytorch
https://paperswithcode.com/paper/paradime-a-framework-for-parametric	ParaDime: A Framework for Parametric Dimensionality Reduction	2210.04582	https://arxiv.org/abs/2210.04582v3	https://arxiv.org/pdf/2210.04582v3.pdf	https://github.com/jku-vds-lab/paradime	true	true	false	pytorch
https://paperswithcode.com/paper/single-index-mixture-cure-model-under	Single-index mixture cure model under monotonicity constraints	2211.09464	https://arxiv.org/abs/2211.09464v2	https://arxiv.org/pdf/2211.09464v2.pdf	https://github.com/tp-yuen/msic	true	true	false	none
https://paperswithcode.com/paper/a-fully-dynamic-algorithm-for-k-regret	A Fully Dynamic Algorithm for k-Regret Minimizing Sets	2005.14493	https://arxiv.org/abs/2005.14493v1	https://arxiv.org/pdf/2005.14493v1.pdf	https://github.com/yhwang1990/dynamic-rms	true	true	true	none
https://paperswithcode.com/paper/semi-supervised-classification-with-graph	Semi-Supervised Classification with Graph Convolutional Networks	1609.02907	http://arxiv.org/abs/1609.02907v4	http://arxiv.org/pdf/1609.02907v4.pdf	https://github.com/kiharalab/gnn_pocket	false	false	true	torch
https://paperswithcode.com/paper/scone-benchmarking-negation-reasoning-in	ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning	2305.19426	https://arxiv.org/abs/2305.19426v1	https://arxiv.org/pdf/2305.19426v1.pdf	https://github.com/selenashe/scone	true	true	false	none
https://paperswithcode.com/paper/sub-word-alignment-is-still-useful-a-vest	Sub-Word Alignment Is Still Useful: A Vest-Pocket Method for Enhancing Low-Resource Machine Translation	2205.04067	https://arxiv.org/abs/2205.04067v1	https://arxiv.org/pdf/2205.04067v1.pdf	https://github.com/Cosmos-Break/transfer-mt-submit	true	false	false	pytorch
https://paperswithcode.com/paper/learning-to-ignore-rethinking-attention-in	Learning to ignore: rethinking attention in CNNs	2111.05684	https://arxiv.org/abs/2111.05684v1	https://arxiv.org/pdf/2111.05684v1.pdf	https://github.com/firasl/inverse_attention	true	false	true	tf
https://paperswithcode.com/paper/interpretable-ai-for-relating-brain	Interpretable AI for relating brain structural and functional connectomes	2210.05672	https://arxiv.org/abs/2210.05672v2	https://arxiv.org/pdf/2210.05672v2.pdf	https://github.com/imkeithyang/staf-gate	true	true	false	pytorch
https://paperswithcode.com/paper/multimodality-multi-lead-ecg-arrhythmia	Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning	2210.06297	https://arxiv.org/abs/2210.06297v1	https://arxiv.org/pdf/2210.06297v1.pdf	https://github.com/uark-aicv/ecg_ssl_12lead	true	true	false	pytorch
https://paperswithcode.com/paper/tts-cgan-a-transformer-time-series	TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentation	2206.13676	https://arxiv.org/abs/2206.13676v1	https://arxiv.org/pdf/2206.13676v1.pdf	https://github.com/imics-lab/tts-cgan	true	true	true	pytorch
https://paperswithcode.com/paper/neural-optimal-transport	Neural Optimal Transport	2201.12220	https://arxiv.org/abs/2201.12220v3	https://arxiv.org/pdf/2201.12220v3.pdf	https://github.com/iamalexkorotin/neuraloptimaltransport	true	true	false	pytorch
https://paperswithcode.com/paper/contextual-instance-decoupling-for-robust	Contextual Instance Decoupling for Robust Multi-Person Pose Estimation	null	http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Contextual_Instance_Decoupling_for_Robust_Multi-Person_Pose_Estimation_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Contextual_Instance_Decoupling_for_Robust_Multi-Person_Pose_Estimation_CVPR_2022_paper.pdf	https://github.com/kennethwdk/cid	true	true	false	pytorch
https://paperswithcode.com/paper/umiformer-mining-the-correlations-between	UMIFormer: Mining the Correlations between Similar Tokens for Multi-View 3D Reconstruction	2302.13987	https://arxiv.org/abs/2302.13987v2	https://arxiv.org/pdf/2302.13987v2.pdf	https://github.com/garyzhu1996/umiformer	true	true	true	pytorch
https://paperswithcode.com/paper/enhancing-weak-lensing-redshift-distribution	Enhancing weak lensing redshift distribution characterization by optimizing the Dark Energy Survey Self-Organizing Map Photo-z method	2408.00922	https://arxiv.org/abs/2408.00922v1	https://arxiv.org/pdf/2408.00922v1.pdf	https://github.com/AndresaCampos/sompz_y6	true	false	true	none
https://paperswithcode.com/paper/emobank-studying-the-impact-of-annotation-1	EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis	2205.01996	https://arxiv.org/abs/2205.01996v1	https://arxiv.org/pdf/2205.01996v1.pdf	https://github.com/JULIELab/EmoBank	true	true	false	none
https://paperswithcode.com/paper/data-driven-feedback-stabilization-of-1	Data-driven control of switched linear systems with probabilistic stability guarantees	2103.10823	https://arxiv.org/abs/2103.10823v2	https://arxiv.org/pdf/2103.10823v2.pdf	https://github.com/zhemingwang/datadrivenswitchcontrol	true	true	false	none
https://paperswithcode.com/paper/m-afl-non-intrusive-feedback-driven-fuzzing	$μ$AFL: Non-intrusive Feedback-driven Fuzzing for Microcontroller Firmware	2202.03013	https://arxiv.org/abs/2202.03013v3	https://arxiv.org/pdf/2202.03013v3.pdf	https://github.com/mcusec/microafl	true	true	true	none
https://paperswithcode.com/paper/zs4ie-a-toolkit-for-zero-shot-information	ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations	2203.13602	https://arxiv.org/abs/2203.13602v3	https://arxiv.org/pdf/2203.13602v3.pdf	https://github.com/osainz59/Ask2Transformers	false	false	true	pytorch
https://paperswithcode.com/paper/trafficqa-a-question-answering-benchmark-and	SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events	2103.15538	https://arxiv.org/abs/2103.15538v3	https://arxiv.org/pdf/2103.15538v3.pdf	https://github.com/MarkHershey/arxiv-dl	false	false	true	none
https://paperswithcode.com/paper/taglets-a-system-for-automatic-semi	TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data	2111.04798	https://arxiv.org/abs/2111.04798v3	https://arxiv.org/pdf/2111.04798v3.pdf	https://github.com/batsresearch/piriyakulkij-mlsys22-code	true	true	true	pytorch
https://paperswithcode.com/paper/hierarchical-representations-and-explicit	Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks	2108.01176	https://arxiv.org/abs/2108.01176v2	https://arxiv.org/pdf/2108.01176v2.pdf	https://github.com/mit-tesse/dsg-rl	true	true	true	pytorch
https://paperswithcode.com/paper/multi-confound-regression-adversarial-network	Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham	2205.02885	https://arxiv.org/abs/2205.02885v2	https://arxiv.org/pdf/2205.02885v2.pdf	https://github.com/mleming/mucran	true	true	true	tf
https://paperswithcode.com/paper/deep-radio-interferometric-imaging-with	Deep Radio Interferometric Imaging with POLISH: DSA-2000 and weak lensing	2111.03249	https://arxiv.org/abs/2111.03249v2	https://arxiv.org/pdf/2111.03249v2.pdf	https://github.com/liamconnor/polish-pub	true	true	true	tf
https://paperswithcode.com/paper/simplifying-approach-to-node-classification	Simplifying approach to Node Classification in Graph Neural Networks	2111.06748	https://arxiv.org/abs/2111.06748v1	https://arxiv.org/pdf/2111.06748v1.pdf	https://github.com/sunilkmaurya/FSGNN	true	true	true	pytorch
https://paperswithcode.com/paper/memorizing-transformers-1	Memorizing Transformers	2203.08913	https://arxiv.org/abs/2203.08913v1	https://arxiv.org/pdf/2203.08913v1.pdf	https://github.com/lucidrains/memorizing-transformers-pytorch	true	false	true	pytorch
https://paperswithcode.com/paper/do-transformers-need-deep-long-range-memory-1	Do Transformers Need Deep Long-Range Memory	2007.03356	https://arxiv.org/abs/2007.03356v1	https://arxiv.org/pdf/2007.03356v1.pdf	https://github.com/lucidrains/memorizing-transformers-pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/deraincyclegan-an-attention-guided	DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking	1912.07015	https://arxiv.org/abs/1912.07015v4	https://arxiv.org/pdf/1912.07015v4.pdf	https://github.com/OaDsis/DerainCycleGAN	true	false	false	pytorch
https://paperswithcode.com/paper/denseclip-extract-free-dense-labels-from-clip	Extract Free Dense Labels from CLIP	2112.01071	https://arxiv.org/abs/2112.01071v2	https://arxiv.org/pdf/2112.01071v2.pdf	https://github.com/chongzhou96/maskclip	true	true	true	pytorch

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.