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/extended-reduced-order-surrogate-models-for	Extended reduced-order surrogate models for scalar-tensor gravity in the strong field and applications to binary pulsars and gravitational waves	2106.01622	https://arxiv.org/abs/2106.01622v2	https://arxiv.org/pdf/2106.01622v2.pdf	https://github.com/mh-guo/pySTGROMX	true	true	false	none
https://paperswithcode.com/paper/polyvector-fields-for-fano-3-folds	Polyvector fields for Fano 3-folds	2104.07626	https://arxiv.org/abs/2104.07626v3	https://arxiv.org/pdf/2104.07626v3.pdf	https://github.com/pbelmans/bivector-fields-fano-3-folds	true	true	false	none
https://paperswithcode.com/paper/bitwidth-adaptive-quantization-aware-neural	Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach	2207.10188	https://arxiv.org/abs/2207.10188v1	https://arxiv.org/pdf/2207.10188v1.pdf	https://github.com/jsjs0369/MEBQAT	true	false	false	pytorch
https://paperswithcode.com/paper/global-hash-tables-strike-back-an-analysis-of	Global Hash Tables Strike Back! An Analysis of Parallel GROUP BY Aggregation	2505.04153	https://arxiv.org/abs/2505.04153v1	https://arxiv.org/pdf/2505.04153v1.pdf	https://github.com/danielxue/global-hash-tables-strike-back	true	true	true	none
https://paperswithcode.com/paper/fast-and-eager-k-medoids-clustering-o-k	Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms	2008.05171	https://arxiv.org/abs/2008.05171v2	https://arxiv.org/pdf/2008.05171v2.pdf	https://github.com/kno10/python-kmedoids	false	false	true	none
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/Zehui127/SQUAD_BERT	false	false	true	tf
https://paperswithcode.com/paper/mlosp-towards-a-unified-implementation-of	mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms	2012.00729	https://arxiv.org/abs/2012.00729v2	https://arxiv.org/pdf/2012.00729v2.pdf	https://github.com/mludkov/mlOSP	true	true	true	none
https://paperswithcode.com/paper/consistency-regularization-and-cutmix-for	Semi-supervised semantic segmentation needs strong, varied perturbations	1906.01916	https://arxiv.org/abs/1906.01916v5	https://arxiv.org/pdf/1906.01916v5.pdf	https://github.com/Britefury/cutmix-semisup-seg	true	true	true	pytorch
https://paperswithcode.com/paper/semantic-style-transfer-and-turning-two-bit	Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks	1603.01768	http://arxiv.org/abs/1603.01768v1	http://arxiv.org/pdf/1603.01768v1.pdf	https://github.com/paulwarkentin/pytorch-neural-doodle	false	false	true	pytorch
https://paperswithcode.com/paper/dgcl-an-efficient-communication-library-for	DGCL: an efficient communication library for distributed GNN training	null	https://dl.acm.org/doi/10.1145/3447786.3456233	https://dl.acm.org/doi/pdf/10.1145/3447786.3456233	https://github.com/czkkkkkk/gccl	false	false	false	none
https://paperswithcode.com/paper/syntheticfur-dataset-for-neural-rendering	SyntheticFur dataset for neural rendering	2105.06409	https://arxiv.org/abs/2105.06409v1	https://arxiv.org/pdf/2105.06409v1.pdf	https://github.com/google-research-datasets/synthetic-fur	true	true	true	none
https://paperswithcode.com/paper/learning-near-optimal-convex-combinations-of	Greedy Convex Ensemble	1910.03742	https://arxiv.org/abs/1910.03742v2	https://arxiv.org/pdf/1910.03742v2.pdf	https://github.com/tan1889/gce	true	true	false	pytorch
https://paperswithcode.com/paper/fisher-rao-metric-geometry-and-complexity-of	Fisher-Rao Metric, Geometry, and Complexity of Neural Networks	1711.01530	http://arxiv.org/abs/1711.01530v2	http://arxiv.org/pdf/1711.01530v2.pdf	https://github.com/ML-KA/PDG-Theory	false	false	true	none
https://paperswithcode.com/paper/neural-moving-horizon-estimation-for-robust	Neural Moving Horizon Estimation for Robust Flight Control	2206.10397	https://arxiv.org/abs/2206.10397v9	https://arxiv.org/pdf/2206.10397v9.pdf	https://github.com/rcl-nus/neuromhe	true	true	true	pytorch
https://paperswithcode.com/paper/brain-signal-classification-via-learning	EEG-based Emotional Video Classification via Learning Connectivity Structure	1905.11678	https://arxiv.org/abs/1905.11678v4	https://arxiv.org/pdf/1905.11678v4.pdf	https://github.com/ELEMKEP/bsc_lcs	true	true	true	pytorch
https://paperswithcode.com/paper/do-the-machine-learning-models-on-a-crowd	Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness	2005.12379	https://arxiv.org/abs/2005.12379v2	https://arxiv.org/pdf/2005.12379v2.pdf	https://github.com/sumonbis/ML-Fairness	true	false	false	none
https://paperswithcode.com/paper/quantum-constraint-problems-can-be-complete	Quantum Constraint Problems can be complete for $\mathsf{BQP}$, $\mathsf{QCMA}$, and more	2101.08381	https://arxiv.org/abs/2101.08381v3	https://arxiv.org/pdf/2101.08381v3.pdf	https://github.com/Timeroot/4StatesIn4Qubits	true	false	false	none
https://paperswithcode.com/paper/170501453	Distributed Proportional-Fairness Control in MicroGrids via Blockchain Smart Contracts	1705.01453	http://arxiv.org/abs/1705.01453v2	http://arxiv.org/pdf/1705.01453v2.pdf	https://github.com/danzipie/fairness-control-contract	false	false	true	none
https://paperswithcode.com/paper/youmakeup-vqa-challenge-towards-fine-grained	YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos	2004.05573	https://arxiv.org/abs/2004.05573v1	https://arxiv.org/pdf/2004.05573v1.pdf	https://github.com/AIM3-RUC/YouMakeup_Baseline	true	true	false	pytorch
https://paperswithcode.com/paper/a-persistence-landscapes-toolbox-for	A persistence landscapes toolbox for topological statistics	1501.00179	http://arxiv.org/abs/1501.00179v3	http://arxiv.org/pdf/1501.00179v3.pdf	https://github.com/queenBNE/Persistent-Landscape-Wrapper	false	false	true	none
https://paperswithcode.com/paper/optimal-market-making-by-reinforcement	Optimal Market Making by Reinforcement Learning	2104.04036	https://arxiv.org/abs/2104.04036v1	https://arxiv.org/pdf/2104.04036v1.pdf	https://github.com/mselser95/optimal-market-making	true	true	false	none
https://paperswithcode.com/paper/a-refined-deep-learning-architecture-for	A Refined Deep Learning Architecture for Diabetic Foot Ulcers Detection	2007.07922	https://arxiv.org/abs/2007.07922v1	https://arxiv.org/pdf/2007.07922v1.pdf	https://github.com/Manugoyal12345/Yet-Another-EfficientDet-Pytorch	true	true	false	pytorch
https://paperswithcode.com/paper/revisiting-data-complexity-metrics-based-on	Revisiting Data Complexity Metrics Based on Morphology for Overlap and Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular Problems Prospect	2007.07935	https://arxiv.org/abs/2007.07935v1	https://arxiv.org/pdf/2007.07935v1.pdf	https://github.com/jdpastri/morphology-metrics	true	true	true	none
https://paperswithcode.com/paper/don-t-stop-pretraining-adapt-language-models	Don't Stop Pretraining: Adapt Language Models to Domains and Tasks	2004.10964	https://arxiv.org/abs/2004.10964v3	https://arxiv.org/pdf/2004.10964v3.pdf	https://github.com/shizhediao/t-dna	false	false	true	pytorch
https://paperswithcode.com/paper/manifold-mixup-better-representations-by	Manifold Mixup: Better Representations by Interpolating Hidden States	1806.05236	https://arxiv.org/abs/1806.05236v7	https://arxiv.org/pdf/1806.05236v7.pdf	https://github.com/rahulmadanahalli/manifold_mixup	false	false	true	tf
https://paperswithcode.com/paper/auto-encoding-variational-bayes	Auto-Encoding Variational Bayes	1312.6114	http://arxiv.org/abs/1312.6114v10	http://arxiv.org/pdf/1312.6114v10.pdf	https://github.com/jarrydmartinx/generative-models	false	false	true	tf
https://paperswithcode.com/paper/carp-compression-through-adaptive-recursive	CARP: Compression through Adaptive Recursive Partitioning for Multi-dimensional Images	1912.05622	https://arxiv.org/abs/1912.05622v2	https://arxiv.org/pdf/1912.05622v2.pdf	https://github.com/xylimeng/CARP	true	true	true	none
https://paperswithcode.com/paper/dart-noise-injection-for-robust-imitation	DART: Noise Injection for Robust Imitation Learning	1703.09327	http://arxiv.org/abs/1703.09327v2	http://arxiv.org/pdf/1703.09327v2.pdf	https://github.com/autonomousvision/data_aggregation	false	false	true	none
https://paperswithcode.com/paper/wasserstein-gan	Wasserstein GAN	1701.07875	http://arxiv.org/abs/1701.07875v3	http://arxiv.org/pdf/1701.07875v3.pdf	https://github.com/catalyst-team/gan	false	false	true	pytorch
https://paperswithcode.com/paper/a-solution-to-the-generalized-ros-hardware-io	A Solution to the Generalized ROS Hardware IO Problem -- A Generic Modbus/TCP Device Driver for PLCs, Sensors and Actuators	2112.11102	https://arxiv.org/abs/2112.11102v1	https://arxiv.org/pdf/2112.11102v1.pdf	https://github.com/bitmeal/ros-modbus-device-driver	true	true	true	none
https://paperswithcode.com/paper/listen-to-look-action-recognition-by	Listen to Look: Action Recognition by Previewing Audio	1912.04487	https://arxiv.org/abs/1912.04487v3	https://arxiv.org/pdf/1912.04487v3.pdf	https://github.com/facebookresearch/Listen-to-Look	false	false	true	pytorch
https://paperswithcode.com/paper/semantic-triples-verbalization-with	Semantic Triples Verbalization with Generative Pre-Training Model	null	https://aclanthology.org/2020.webnlg-1.17	https://aclanthology.org/2020.webnlg-1.17.pdf	https://github.com/blinovpd/ru-rdf2text	true	false	false	pytorch
https://paperswithcode.com/paper/atomic-loans-cryptocurrency-debt-instruments	Atomic Loans: Cryptocurrency Debt Instruments	1901.05117	http://arxiv.org/abs/1901.05117v1	http://arxiv.org/pdf/1901.05117v1.pdf	https://github.com/AtomicLoans/technicalpaper	false	false	true	none
https://paperswithcode.com/paper/student-performance-prediction-using-dynamic	Student Performance Prediction Using Dynamic Neural Models	2106.00524	https://arxiv.org/abs/2106.00524v1	https://arxiv.org/pdf/2106.00524v1.pdf	https://github.com/delmarin35/Dynamic-Neural-Models-for-Knowledge-Tracing	true	true	false	none
https://paperswithcode.com/paper/animegan-a-novel-lightweight-gan-for-photo	AnimeGAN: A Novel Lightweight GAN for Photo Animation	null	https://link.springer.com/chapter/10.1007/978-981-15-5577-0_18	https://link.springer.com/chapter/10.1007/978-981-15-5577-0_18	https://github.com/mindspore-courses/heads-on-mindspore/tree/main/2-AnimeGAN	false	false	false	mindspore
https://paperswithcode.com/paper/from-words-to-sound-neural-audio-synthesis-of	From Words to Sound: Neural Audio Synthesis of Guitar Sounds with Timbral Descriptors	null	https://zenodo.org/record/7088416	https://zenodo.org/record/7088416	https://github.com/TheSoundOfAIOSR/thesoundofaiosr.github.io	false	false	false	none
https://paperswithcode.com/paper/deep-cnns-with-spatially-weighted-pooling-for	Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition	null	https://ieeexplore.ieee.org/document/7891907	https://www.researchgate.net/profile/Qichang-Hu/publication/316027349_Deep_CNNs_With_Spatially_Weighted_Pooling_for_Fine-Grained_Car_Recognition/links/59da13dca6fdcc2aad1299eb/Deep-CNNs-With-Spatially-Weighted-Pooling-for-Fine-Grained-Car-Recognition.pdf?_sg%5B0%5D=kFfa3QAo81iOIGlcjQ8XRVrfle6Ja-f3PbBzcCVIn3hbSh6EvHLERWho98fUz31FG9fT0TblP-aepGOCPxoarQ.OjIShztuvZs6W2EaIPef4wBuCkjA7vhzJphfFK-0w1_CjLGnxrWAUXxW4JP-7CEbBxDP3jW_tMo-sBuVDJfDqQ&_sg%5B1%5D=VMpD6s3ZN7MRfqLrLI8TiDC4DPHlksWNxOtrIPd7m-hc6H8V3yhKpndR7TsXCFyoHW8KFaQN-R7LmMcq-GO55-TxkzshV7BCIBpLq159AsWm.OjIShztuvZs6W2EaIPef4wBuCkjA7vhzJphfFK-0w1_CjLGnxrWAUXxW4JP-7CEbBxDP3jW_tMo-sBuVDJfDqQ&_iepl=	https://github.com/duongttr/SWP	false	false	false	tf
https://paperswithcode.com/paper/modeling-constrained-preemption-dynamics-of	Modeling Constrained Preemption Dynamics Of Transient Cloud Servers	1911.05160	https://arxiv.org/abs/1911.05160v1	https://arxiv.org/pdf/1911.05160v1.pdf	https://github.com/kadupitiya/goog-preemption-data	false	false	true	none
https://paperswithcode.com/paper/some-stylometric-remarks-on-ovid-s-heroides	Some Stylometric Remarks on Ovid's Heroides and the Epistula Sapphus	2202.11864	https://arxiv.org/abs/2202.11864v1	https://arxiv.org/pdf/2202.11864v1.pdf	https://github.com/bnagy/heroides-paper	true	true	false	none
https://paperswithcode.com/paper/amr-parsing-as-sequence-to-graph-transduction	AMR Parsing as Sequence-to-Graph Transduction	1905.08704	https://arxiv.org/abs/1905.08704v2	https://arxiv.org/pdf/1905.08704v2.pdf	https://github.com/sheng-z/stog	true	true	true	pytorch
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/jeremy313/Soteria	false	false	true	pytorch
https://paperswithcode.com/paper/clusttr-clustering-training-for-robustness	Rethinking Clustering for Robustness	2006.07682	https://arxiv.org/abs/2006.07682v3	https://arxiv.org/pdf/2006.07682v3.pdf	https://github.com/clustr-official-account/ClusTR-Clustering-Training-For-Robustness	true	true	true	pytorch
https://paperswithcode.com/paper/visual-chirality-1	Visual Chirality	2006.09512	https://arxiv.org/abs/2006.09512v1	https://arxiv.org/pdf/2006.09512v1.pdf	https://github.com/linzhiqiu/digital_chirality	true	true	false	pytorch
https://paperswithcode.com/paper/partial-policy-iteration-for-l1-robust-markov	Partial Policy Iteration for L1-Robust Markov Decision Processes	2006.09484	https://arxiv.org/abs/2006.09484v1	https://arxiv.org/pdf/2006.09484v1.pdf	https://github.com/marekpetrik/PPI_paper	true	true	false	none
https://paperswithcode.com/paper/phase-aware-speech-enhancement-with-deep-1	Phase-aware Speech Enhancement with Deep Complex U-Net	1903.03107	http://arxiv.org/abs/1903.03107v2	http://arxiv.org/pdf/1903.03107v2.pdf	https://github.com/chanil1218/DCUnet.pytorch	false	false	false	pytorch
https://paperswithcode.com/paper/the-gh-exin-neural-network-for-hierarchical	The GH-EXIN neural network for hierarchical clustering	null	https://www.sciencedirect.com/science/article/pii/S0893608019302060	https://www.sciencedirect.com/science/article/pii/S0893608019302060	https://github.com/pietrobarbiero/ghexin	false	false	false	none
https://paperswithcode.com/paper/urcdm-ultra-resolution-image-synthesis-in	URCDM: Ultra-Resolution Image Synthesis in Histopathology	2407.13277	https://arxiv.org/abs/2407.13277v1	https://arxiv.org/pdf/2407.13277v1.pdf	https://github.com/scechnicka/URCDM	true	false	true	pytorch
https://paperswithcode.com/paper/hsemotion-team-at-the-7th-abaw-challenge	HSEmotion Team at the 7th ABAW Challenge: Multi-Task Learning and Compound Facial Expression Recognition	2407.13184	https://arxiv.org/abs/2407.13184v1	https://arxiv.org/pdf/2407.13184v1.pdf	https://github.com/HSE-asavchenko/face-emotion-recognition	true	true	false	tf
https://paperswithcode.com/paper/neural-architecture-retrieval	Neural Architecture Retrieval	2307.07919	https://arxiv.org/abs/2307.07919v2	https://arxiv.org/pdf/2307.07919v2.pdf	https://github.com/terrypei/nnretrieval	true	true	false	pytorch
https://paperswithcode.com/paper/rt-bene-a-dataset-and-baselines-for-real-time	RT-BENE: A Dataset and Baselines for Real-Time Blink Estimation in Natural Environments	null	http://openaccess.thecvf.com/content_ICCVW_2019/html/GAZE/Cortacero_RT-BENE_A_Dataset_and_Baselines_for_Real-Time_Blink_Estimation_in_ICCVW_2019_paper.html	http://openaccess.thecvf.com/content_ICCVW_2019/papers/GAZE/Cortacero_RT-BENE_A_Dataset_and_Baselines_for_Real-Time_Blink_Estimation_in_ICCVW_2019_paper.pdf	https://github.com/Tobias-Fischer/rt_gene	false	false	false	tf
https://paperswithcode.com/paper/robot-localization-in-floor-plans-using-a	Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network	1903.01804	https://arxiv.org/abs/1903.01804v2	https://arxiv.org/pdf/1903.01804v2.pdf	https://github.com/ayusefi/Localization-Papers	false	false	true	none
https://paperswithcode.com/paper/training-deep-neural-networks-on-noisy-labels	Training Deep Neural Networks on Noisy Labels with Bootstrapping	1412.6596	http://arxiv.org/abs/1412.6596v3	http://arxiv.org/pdf/1412.6596v3.pdf	https://github.com/dr-darryl-wright/Noisy-Labels-with-Bootstrapping	false	false	true	none
https://paperswithcode.com/paper/efficientdet-scalable-and-efficient-object	EfficientDet: Scalable and Efficient Object Detection	1911.09070	https://arxiv.org/abs/1911.09070v7	https://arxiv.org/pdf/1911.09070v7.pdf	https://github.com/Manugoyal12345/Yet-Another-EfficientDet-Pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/adahessian-an-adaptive-second-order-optimizer	ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning	2006.00719	https://arxiv.org/abs/2006.00719v3	https://arxiv.org/pdf/2006.00719v3.pdf	https://github.com/amirgholami/adahessian	true	true	true	pytorch
https://paperswithcode.com/paper/picar-an-efficient-extendable-approach-for	PICAR: An Efficient Extendable Approach for Fitting Hierarchical Spatial Models	1912.02382	https://arxiv.org/abs/1912.02382v2	https://arxiv.org/pdf/1912.02382v2.pdf	https://github.com/benee55/PICAR_code	true	true	false	none
https://paperswithcode.com/paper/a-systematic-approach-to-robustness-modelling	A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (TRASHFIRE)	2401.13751	https://arxiv.org/abs/2401.13751v2	https://arxiv.org/pdf/2401.13751v2.pdf	https://github.com/simplymathematics/deckard/tree/main/examples/pytorch	false	false	false	pytorch
https://paperswithcode.com/paper/human-whole-body-dynamics-estimation-for	Human Whole-Body Dynamics Estimation for Enhancing Physical Human-Robot Interaction	1912.01136	https://arxiv.org/abs/1912.01136v1	https://arxiv.org/pdf/1912.01136v1.pdf	https://github.com/claudia-lat/MAPest	true	true	true	none
https://paperswithcode.com/paper/temporal-cycle-consistency-learning	Temporal Cycle-Consistency Learning	1904.07846	http://arxiv.org/abs/1904.07846v1	http://arxiv.org/pdf/1904.07846v1.pdf	https://github.com/google-research/google-research/tree/master/tcc	false	false	true	tf
https://paperswithcode.com/paper/mixup-beyond-empirical-risk-minimization	mixup: Beyond Empirical Risk Minimization	1710.09412	http://arxiv.org/abs/1710.09412v2	http://arxiv.org/pdf/1710.09412v2.pdf	https://github.com/CaoShuning/MIXUP	false	false	true	pytorch
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/chandra411/Product-Detection	false	false	true	tf
https://paperswithcode.com/paper/high-efficiency-calculation-of-elastic	Investigating elastic constants across diverse strain-matrix sets	2002.00005	https://arxiv.org/abs/2002.00005v2	https://arxiv.org/pdf/2002.00005v2.pdf	https://github.com/zhongliliu/elastool	false	false	true	none
https://paperswithcode.com/paper/meta-learning-framework-with-applications-to	Meta-learning framework with applications to zero-shot time-series forecasting	2002.02887	https://arxiv.org/abs/2002.02887v3	https://arxiv.org/pdf/2002.02887v3.pdf	https://github.com/dmitri-carpov/deepar_evaluation	false	false	true	mxnet
https://paperswithcode.com/paper/auto-encoding-variational-bayes	Auto-Encoding Variational Bayes	1312.6114	http://arxiv.org/abs/1312.6114v10	http://arxiv.org/pdf/1312.6114v10.pdf	https://github.com/sarus-tech/tf2-published-models	false	false	false	tf
https://paperswithcode.com/paper/quaternion-equivariant-capsule-networks-for-1	Quaternion Equivariant Capsule Networks for 3D Point Clouds	1912.12098	https://arxiv.org/abs/1912.12098v3	https://arxiv.org/pdf/1912.12098v3.pdf	https://github.com/tolgabirdal/qecnetworks	false	false	true	pytorch
https://paperswithcode.com/paper/phase-transitions-of-wave-packet-dynamics-in	Phase transitions of wave packet dynamics in disordered non-Hermitian systems	2301.07370	https://arxiv.org/abs/2301.07370v2	https://arxiv.org/pdf/2301.07370v2.pdf	https://zenodo.org/record/7535012	false	false	false	none
https://paperswithcode.com/paper/beyond-graph-neural-networks-with-lifted	Beyond Graph Neural Networks with Lifted Relational Neural Networks	2007.06286	https://arxiv.org/abs/2007.06286v1	https://arxiv.org/pdf/2007.06286v1.pdf	https://github.com/GustikS/GNNwLRNNs	true	true	false	pytorch
https://paperswithcode.com/paper/i2l-meshnet-image-to-lixel-prediction-network-1	I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image	2008.03713	https://arxiv.org/abs/2008.03713v2	https://arxiv.org/pdf/2008.03713v2.pdf	https://github.com/mks0601/I2L-MeshNet_RELEASE	true	true	true	pytorch
https://paperswithcode.com/paper/spatial-semantic-embedding-network-fast-3d	Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning	2007.03169	https://arxiv.org/abs/2007.03169v1	https://arxiv.org/pdf/2007.03169v1.pdf	https://github.com/96lives/ssen	true	true	true	pytorch
https://paperswithcode.com/paper/meta-learning-representations-for-continual	Meta-Learning Representations for Continual Learning	1905.12588	https://arxiv.org/abs/1905.12588v2	https://arxiv.org/pdf/1905.12588v2.pdf	https://github.com/lexili24/NLUProject	false	false	true	pytorch
https://paperswithcode.com/paper/cellular-automaton-decoders-with-provable	Cellular-automaton decoders with provable thresholds for topological codes	1809.10145	https://arxiv.org/abs/1809.10145v1	https://arxiv.org/pdf/1809.10145v1.pdf	https://github.com/MikeVasmer/Sweep-Decoder-Boundaries	false	false	true	none
https://paperswithcode.com/paper/harmonic-networks-deep-translation-and	Harmonic Networks: Deep Translation and Rotation Equivariance	1612.04642	http://arxiv.org/abs/1612.04642v2	http://arxiv.org/pdf/1612.04642v2.pdf	https://github.com/deworrall92/harmonicConvolutions	false	false	true	tf
https://paperswithcode.com/paper/real-world-attack-on-mtcnn-face-detection	Real-world adversarial attack on MTCNN face detection system	1910.06261	https://arxiv.org/abs/1910.06261v2	https://arxiv.org/pdf/1910.06261v2.pdf	https://github.com/Mind23-2/MindCode-101/tree/main/MTCNN	false	false	false	mindspore
https://paperswithcode.com/paper/a-discrete-representation-of-einsteins	A Discrete Representation of Einstein's Geometric Theory of Gravitation: The Fundamental Role of Dual Tessellations in Regge Calculus	0804.0279	http://arxiv.org/abs/0804.0279v1	http://arxiv.org/pdf/0804.0279v1.pdf	https://github.com/EelcoHoogendoorn/pycomplex	false	false	true	none
https://paperswithcode.com/paper/paragraph-level-neural-question-generation	Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks	null	https://aclanthology.org/D18-1424	https://aclanthology.org/D18-1424.pdf	https://github.com/seanie12/neural-question-generation	false	false	false	pytorch
https://paperswithcode.com/paper/geometry-aware-supertagging-with	Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions	2203.12235	https://arxiv.org/abs/2203.12235v3	https://arxiv.org/pdf/2203.12235v3.pdf	https://github.com/konstantinoskokos/dynamic-graph-supertagging	true	true	true	pytorch
https://paperswithcode.com/paper/clones-in-deep-learning-code-what-where-and	Clones in Deep Learning Code: What, Where, and Why?	2107.13614	https://arxiv.org/abs/2107.13614v1	https://arxiv.org/pdf/2107.13614v1.pdf	https://github.com/Hadhemii/ClonesInDLCode	true	true	false	none
https://paperswithcode.com/paper/interpretable-and-transferable-models-to	Interpretable and Transferable Models to Understand the Impact of Lockdown Measures on Local Air Quality	2011.10144	https://arxiv.org/abs/2011.10144v2	https://arxiv.org/pdf/2011.10144v2.pdf	https://github.com/johanna-einsiedler/covid-19-air-pollution	true	true	true	none
https://paperswithcode.com/paper/out-of-distribution-detection-with-energy	Master's Thesis: Out-of-distribution Detection with Energy-based Models	2302.12002	https://arxiv.org/abs/2302.12002v2	https://arxiv.org/pdf/2302.12002v2.pdf	https://github.com/selflein/ma-ebm	true	true	true	pytorch
https://paperswithcode.com/paper/emergence-and-stability-of-self-evolved	Emergence and Stability of Self-Evolved Cooperative Strategies using Stochastic Machines	2010.13024	https://arxiv.org/abs/2010.13024v1	https://arxiv.org/pdf/2010.13024v1.pdf	https://github.com/jinhongkuan/evol-sim	true	true	false	none
https://paperswithcode.com/paper/joint-power-control-and-lsfd-for-wireless	Joint Power Control and LSFD for Wireless-Powered Cell-Free Massive MIMO	2002.09270	https://arxiv.org/abs/2002.09270v2	https://arxiv.org/pdf/2002.09270v2.pdf	https://github.com/emilbjornson/wireless-powered-cell-free	true	true	true	none
https://paperswithcode.com/paper/bottom-up-and-top-down-attention-for-image	Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering	1707.07998	http://arxiv.org/abs/1707.07998v3	http://arxiv.org/pdf/1707.07998v3.pdf	https://github.com/xiaobai714/image_caption	false	false	true	pytorch
https://paperswithcode.com/paper/neural-machine-translating-from-natural	Neural Machine Translating from Natural Language to SPARQL	1906.09302	https://arxiv.org/abs/1906.09302v1	https://arxiv.org/pdf/1906.09302v1.pdf	https://github.com/xiaoyuin/tntspa	false	false	true	tf
https://paperswithcode.com/paper/semantic-histogram-based-graph-matching-for	Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment	2010.09297	https://arxiv.org/abs/2010.09297v2	https://arxiv.org/pdf/2010.09297v2.pdf	https://github.com/gxytcrc/Semantic-Graph-based--global-Localization	false	false	true	none
https://paperswithcode.com/paper/adahessian-an-adaptive-second-order-optimizer	ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning	2006.00719	https://arxiv.org/abs/2006.00719v3	https://arxiv.org/pdf/2006.00719v3.pdf	https://github.com/morganmcg1/ImageNette_ImageWoof_ImageWang	false	false	true	none
https://paperswithcode.com/paper/normalization-matters-in-weakly-supervised	Normalization Matters in Weakly Supervised Object Localization	2107.13221	https://arxiv.org/abs/2107.13221v1	https://arxiv.org/pdf/2107.13221v1.pdf	https://github.com/GenDisc/IVR	false	false	true	pytorch
https://paperswithcode.com/paper/vehicle-and-license-plate-recognition-with	Vehicle and License Plate Recognition with Novel Dataset for Toll Collection	2202.05631	https://arxiv.org/abs/2202.05631v2	https://arxiv.org/pdf/2202.05631v2.pdf	https://dagshub.com/arnavr.neo/VT-LPR	false	false	false	none
https://paperswithcode.com/paper/real-time-mdnet	Real-Time MDNet	1808.08834	http://arxiv.org/abs/1808.08834v1	http://arxiv.org/pdf/1808.08834v1.pdf	https://github.com/Amgao/RLS-RTMDNet	false	false	true	pytorch
https://paperswithcode.com/paper/sequence-tagging-with-contextual-and-non	Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation	1906.01569	https://arxiv.org/abs/1906.01569v1	https://arxiv.org/pdf/1906.01569v1.pdf	https://github.com/bheinzerling/subword-sequence-tagging	true	false	false	pytorch
https://paperswithcode.com/paper/coordinated-exploration-via-intrinsic-rewards	Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning	1905.12127	https://arxiv.org/abs/1905.12127v3	https://arxiv.org/pdf/1905.12127v3.pdf	https://github.com/shariqiqbal2810/Multi-Explore	true	true	true	pytorch
https://paperswithcode.com/paper/certainty-equivalent-perception-based-control	Certainty Equivalent Perception-Based Control	2008.12332	https://arxiv.org/abs/2008.12332v2	https://arxiv.org/pdf/2008.12332v2.pdf	https://github.com/modestyachts/certainty_equiv_perception_control	true	true	false	none
https://paperswithcode.com/paper/factorised-representation-learning-in-cardiac	Disentangled Representation Learning in Cardiac Image Analysis	1903.09467	https://arxiv.org/abs/1903.09467v4	https://arxiv.org/pdf/1903.09467v4.pdf	https://github.com/TsaftarisCollaboratory/CSDisentanglement_Metrics_Library	false	false	true	pytorch
https://paperswithcode.com/paper/fast-deep-reinforcement-learning-using-online	Fast deep reinforcement learning using online adjustments from the past	1810.08163	http://arxiv.org/abs/1810.08163v1	http://arxiv.org/pdf/1810.08163v1.pdf	https://github.com/AnnaNikitaRL/EVA	false	false	true	pytorch
https://paperswithcode.com/paper/machine-learning-based-generalized-model-for	Machine Learning-Based Generalized Model for Finite Element Analysis of Roll Deflection During the Austenitic Stainless Steel 316L Strip Rolling	2102.02470	https://arxiv.org/abs/2102.02470v2	https://arxiv.org/pdf/2102.02470v2.pdf	https://github.com/mahshadlotfinia/Stress316L	true	true	true	none
https://paperswithcode.com/paper/kinship-identification-through-joint-learning	Kinship Identification through Joint Learning Using Kinship Verification Ensembles	2004.06382	https://arxiv.org/abs/2004.06382v4	https://arxiv.org/pdf/2004.06382v4.pdf	https://github.com/we-wan/JLNet	false	false	true	pytorch
https://paperswithcode.com/paper/parallel-streaming-wasserstein-barycenters	Parallel Streaming Wasserstein Barycenters	1705.07443	http://arxiv.org/abs/1705.07443v2	http://arxiv.org/pdf/1705.07443v2.pdf	https://github.com/mstaib/stochastic-barycenter-code	true	true	true	none
https://paperswithcode.com/paper/pano-avqa-grounded-audio-visual-question-1	Pano-AVQA: Grounded Audio-Visual Question Answering on 360$^\circ$ Videos	2110.05122	https://arxiv.org/abs/2110.05122v1	https://arxiv.org/pdf/2110.05122v1.pdf	https://github.com/hs-yn/panoavqa	true	true	false	pytorch
https://paperswithcode.com/paper/prepended-domain-transformer-heterogeneous	Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles	2210.06529	https://arxiv.org/abs/2210.06529v1	https://arxiv.org/pdf/2210.06529v1.pdf	https://github.com/anjith2006/bob.paper.tifs2022_hfr_prepended_domain_transformer	false	false	false	none
https://paperswithcode.com/paper/why-not-simply-translate-a-first-swedish	Why Not Simply Translate? A First Swedish Evaluation Benchmark for Semantic Similarity	2009.03116	https://arxiv.org/abs/2009.03116v2	https://arxiv.org/pdf/2009.03116v2.pdf	https://github.com/timpal0l/sts-benchmark-swedish	true	true	false	none
https://paperswithcode.com/paper/solving-classification-problems-using	Solving classification problems using Traceless Genetic Programming	2111.14790	https://arxiv.org/abs/2111.14790v1	https://arxiv.org/pdf/2111.14790v1.pdf	https://github.com/mihaioltean/traceless-genetic-programming	true	true	false	none
https://paperswithcode.com/paper/performance-of-openbci-eeg-binary-intent	Performance of OpenBCI EEG Binary Intent Classification with Laryngeal Imagery	2107.00045	https://arxiv.org/abs/2107.00045v1	https://arxiv.org/pdf/2107.00045v1.pdf	https://github.com/nateGeorge/openbci_laryngeal_imagery	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.