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/multiple-attribute-text-style-transfer	Multiple-Attribute Text Style Transfer	1811.00552	https://arxiv.org/abs/1811.00552v2	https://arxiv.org/pdf/1811.00552v2.pdf	https://github.com/clock-me/text-restyle	false	false	true	pytorch
https://paperswithcode.com/paper/shellcode-ia32-a-dataset-for-automatic	Shellcode_IA32: A Dataset for Automatic Shellcode Generation	2104.13100	https://arxiv.org/abs/2104.13100v4	https://arxiv.org/pdf/2104.13100v4.pdf	https://github.com/dessertlab/Shellcode_IA32	true	true	true	none
https://paperswithcode.com/paper/staktau-profiling-hpc-applications-operating	STaKTAU: profiling HPC applications' operating system usage	2304.11205	https://arxiv.org/abs/2304.11205v1	https://arxiv.org/pdf/2304.11205v1.pdf	https://github.com/coti/staktau	true	true	false	none
https://paperswithcode.com/paper/meta-learners-for-estimating-heterogeneous	Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning	1706.03461	http://arxiv.org/abs/1706.03461v5	http://arxiv.org/pdf/1706.03461v5.pdf	https://github.com/forestry-labs/causalToolbox	false	false	true	none
https://paperswithcode.com/paper/information-theoretic-stochastic-contrastive-1	Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN	2112.09653	https://arxiv.org/abs/2112.09653v1	https://arxiv.org/pdf/2112.09653v1.pdf	https://github.com/vkinakh/InfoSCC-GAN	true	true	false	pytorch
https://paperswithcode.com/paper/ernet-efficient-and-reliable-human-object	ERNet: Efficient and Reliable Human-Object Interaction Detection	null	https://ieeexplore.ieee.org/abstract/document/10026602	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10026602	https://github.com/Monash-CyPhi-AI-Research-Lab/ernet	false	true	false	pytorch
https://paperswithcode.com/paper/coherent-semantic-attention-for-image	Coherent Semantic Attention for Image Inpainting	1905.12384	https://arxiv.org/abs/1905.12384v3	https://arxiv.org/pdf/1905.12384v3.pdf	https://github.com/yangyucheng000/University/tree/main/model-2/cohere	false	false	false	mindspore
https://paperswithcode.com/paper/utterance-level-aggregation-for-speaker	Utterance-level Aggregation For Speaker Recognition In The Wild	1902.10107	https://arxiv.org/abs/1902.10107v2	https://arxiv.org/pdf/1902.10107v2.pdf	https://github.com/khassanoff/Speaker_Verification	false	false	true	pytorch
https://paperswithcode.com/paper/exactly-computing-the-local-lipschitz	Exactly Computing the Local Lipschitz Constant of ReLU Networks	2003.01219	https://arxiv.org/abs/2003.01219v2	https://arxiv.org/pdf/2003.01219v2.pdf	https://github.com/revbucket/lipMIP	false	false	true	pytorch
https://paperswithcode.com/paper/neural-network-verification-in-control	Neural Network Verification in Control	2110.01388	https://arxiv.org/abs/2110.01388v1	https://arxiv.org/pdf/2110.01388v1.pdf	https://github.com/mit-acl/nn_robustness_analysis	true	true	false	pytorch
https://paperswithcode.com/paper/of-models-and-tin-men-a-behavioural-economics	Of Models and Tin Men: A Behavioural Economics Study of Principal-Agent Problems in AI Alignment using Large-Language Models	2307.11137	https://arxiv.org/abs/2307.11137v3	https://arxiv.org/pdf/2307.11137v3.pdf	https://github.com/phelps-sg/llm-cooperation	true	true	true	none
https://paperswithcode.com/paper/effectiveness-of-anonymization-in-double	Effectiveness of Anonymization in Double-Blind Review	1709.01609	https://arxiv.org/abs/1709.01609v1	https://arxiv.org/pdf/1709.01609v1.pdf	https://github.com/double-blind-reviewing/double-blind-reviewing.github.io	false	false	true	none
https://paperswithcode.com/paper/padim-a-patch-distribution-modeling-framework	PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization	2011.08785	https://arxiv.org/abs/2011.08785v1	https://arxiv.org/pdf/2011.08785v1.pdf	https://github.com/koheitokda/PaDiM	false	false	true	pytorch
https://paperswithcode.com/paper/model-construction-for-convex-constrained	Model Construction for Convex-Constrained Derivative-Free Optimization	2403.14960	https://arxiv.org/abs/2403.14960v1	https://arxiv.org/pdf/2403.14960v1.pdf	https://github.com/numericalalgorithmsgroup/pybobyqa	false	false	true	none
https://paperswithcode.com/paper/mixmatch-a-holistic-approach-to-semi	MixMatch: A Holistic Approach to Semi-Supervised Learning	1905.02249	https://arxiv.org/abs/1905.02249v2	https://arxiv.org/pdf/1905.02249v2.pdf	https://github.com/smkim7-kr/albu-MixMatch-pytorch	false	false	true	pytorch
https://paperswithcode.com/paper/aspect-sentiment-triplet-extraction-using	Aspect Sentiment Triplet Extraction Using Reinforcement Learning	2108.06107	https://arxiv.org/abs/2108.06107v1	https://arxiv.org/pdf/2108.06107v1.pdf	https://github.com/declare-lab/aste-rl	true	true	true	pytorch
https://paperswithcode.com/paper/edgeformer-improving-light-weight-convnets-by	ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer	2203.03952	https://arxiv.org/abs/2203.03952v5	https://arxiv.org/pdf/2203.03952v5.pdf	https://github.com/hkzhang91/edgeformer	true	true	true	pytorch
https://paperswithcode.com/paper/optimal-multiple-change-point-detection-for	Optimal multiple change-point detection for high-dimensional data	2011.07818	https://arxiv.org/abs/2011.07818v2	https://arxiv.org/pdf/2011.07818v2.pdf	https://github.com/epilliat/multicpdetec	true	true	false	pytorch
https://paperswithcode.com/paper/tower-data-structures-in-quantum	Tower: Data Structures in Quantum Superposition	2205.10255	https://arxiv.org/abs/2205.10255v3	https://arxiv.org/pdf/2205.10255v3.pdf	https://github.com/psg-mit/tower-oopsla22	true	true	false	none
https://paperswithcode.com/paper/deepsplit-scalable-verification-of-deep	DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting	2106.09117	https://arxiv.org/abs/2106.09117v3	https://arxiv.org/pdf/2106.09117v3.pdf	https://github.com/shaoruchen/deepsplit	true	true	true	pytorch
https://paperswithcode.com/paper/post-selection-inference-for-l1-penalized	Post-selection inference for L1-penalized likelihood models	1602.07358	http://arxiv.org/abs/1602.07358v3	http://arxiv.org/pdf/1602.07358v3.pdf	https://github.com/quentin-duchemin/sigle	false	false	true	none
https://paperswithcode.com/paper/relating-human-perception-of-musicality-to	Relating Human Perception of Musicality to Prediction in a Predictive Coding Model	2210.16587	https://arxiv.org/abs/2210.16587v1	https://arxiv.org/pdf/2210.16587v1.pdf	https://github.com/nikolasmcneal/music-prediction	false	false	true	pytorch
https://paperswithcode.com/paper/very-deep-convolutional-networks-for-large	Very Deep Convolutional Networks for Large-Scale Image Recognition	1409.1556	http://arxiv.org/abs/1409.1556v6	http://arxiv.org/pdf/1409.1556v6.pdf	https://github.com/jiweibo/imagenet	false	false	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/jiweibo/imagenet	false	false	true	pytorch
https://paperswithcode.com/paper/graph-convolutional-matrix-completion	Graph Convolutional Matrix Completion	1706.02263	http://arxiv.org/abs/1706.02263v2	http://arxiv.org/pdf/1706.02263v2.pdf	https://github.com/OweysMomenzada/Graph-Neural-Networks-for-effecient-Recommender-Systems	false	false	true	pytorch
https://paperswithcode.com/paper/objects-as-points	Objects as Points	1904.07850	http://arxiv.org/abs/1904.07850v2	http://arxiv.org/pdf/1904.07850v2.pdf	https://github.com/mindspore-ai/models/tree/master/research/cv/centernet_resnet50_v1	false	false	false	mindspore
https://paperswithcode.com/paper/dyverse-dynamic-vertical-scaling-in-multi	DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments	1810.04608	http://arxiv.org/abs/1810.04608v1	http://arxiv.org/pdf/1810.04608v1.pdf	https://github.com/qub-blesson/DYVERSE	false	false	true	none
https://paperswithcode.com/paper/generalization-without-systematicity-on-the	Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks	1711.00350	http://arxiv.org/abs/1711.00350v3	http://arxiv.org/pdf/1711.00350v3.pdf	https://github.com/yoonkim/neural-qcfg	false	false	true	pytorch
https://paperswithcode.com/paper/styleptb-a-compositional-benchmark-for-fine	StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer	2104.05196	https://arxiv.org/abs/2104.05196v1	https://arxiv.org/pdf/2104.05196v1.pdf	https://github.com/yoonkim/neural-qcfg	false	false	true	pytorch
https://paperswithcode.com/paper/special-subsets-of-addresses-for-blockchains	Special subsets of addresses for blockchains using the secp256k1 curve	2206.14107	https://arxiv.org/abs/2206.14107v1	https://arxiv.org/pdf/2206.14107v1.pdf	https://github.com/gitgab19/blockchain_addresses_list	true	true	true	none
https://paperswithcode.com/paper/joint-inference-of-multiple-graphs-with	Joint inference of multiple graphs with hidden variables from stationary graph signals	2110.03666	https://arxiv.org/abs/2110.03666v2	https://arxiv.org/pdf/2110.03666v2.pdf	https://github.com/reysam93/hidden_joint_inference	true	true	false	none
https://paperswithcode.com/paper/verifiable-smart-contract-portability	Verifiable Smart Contract Portability	1902.03868	http://arxiv.org/abs/1902.03868v1	http://arxiv.org/pdf/1902.03868v1.pdf	https://github.com/informartin/VeriSmart	true	true	true	none
https://paperswithcode.com/paper/deep-particulate-matter-forecasting-model	Deep Particulate Matter Forecasting Model Using Correntropy-Induced Loss	2106.03032	https://arxiv.org/abs/2106.03032v2	https://arxiv.org/pdf/2106.03032v2.pdf	https://github.com/appleparan/mise.py	true	true	true	pytorch
https://paperswithcode.com/paper/modeling-long-and-short-term-temporal	Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks	1703.07015	http://arxiv.org/abs/1703.07015v3	http://arxiv.org/pdf/1703.07015v3.pdf	https://github.com/appleparan/mise.py	false	false	true	pytorch
https://paperswithcode.com/paper/aegis-mitigating-targeted-bit-flip-attacks	Aegis: Mitigating Targeted Bit-flip Attacks against Deep Neural Networks	2302.13520	https://arxiv.org/abs/2302.13520v1	https://arxiv.org/pdf/2302.13520v1.pdf	https://github.com/wjl123wjl/aegis	true	true	false	pytorch
https://paperswithcode.com/paper/knowledge-graph-informed-fake-news	Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles	2110.10457	https://arxiv.org/abs/2110.10457v2	https://arxiv.org/pdf/2110.10457v2.pdf	https://gitlab.com/boshko.koloski/codename_fn_b	true	false	false	pytorch
https://paperswithcode.com/paper/a-study-in-mathbb-g-mathbb-r-geq-0-from-the	A study in $\mathbb{G}_{\mathbb{R}, \geq 0}$: from the geometric case book of Wilson loop diagrams and SYM $N=4$	1803.00958	https://arxiv.org/abs/1803.00958v1	https://arxiv.org/pdf/1803.00958v1.pdf	https://github.com/zeefryer/positroids	false	false	true	none
https://paperswithcode.com/paper/deep-analysis-on-subgraph-isomorphism	Deep Analysis on Subgraph Isomorphism	2012.06802	https://arxiv.org/abs/2012.06802v2	https://arxiv.org/pdf/2012.06802v2.pdf	https://github.com/bookug/siep	true	false	true	none
https://paperswithcode.com/paper/optimal-monomial-quadratization-for-ode	Optimal monomial quadratization for ODE systems	2103.08013	https://arxiv.org/abs/2103.08013v3	https://arxiv.org/pdf/2103.08013v3.pdf	https://github.com/AndreyBychkov/QBee	true	true	true	none
https://paperswithcode.com/paper/distance-function-for-spike-prediction	Spike distance function as a learning objective for spike prediction	2312.01966	https://arxiv.org/abs/2312.01966v2	https://arxiv.org/pdf/2312.01966v2.pdf	https://github.com/kevindoran/spikedistance	true	true	false	pytorch
https://paperswithcode.com/paper/validating-simulations-of-user-query-variants	Validating Simulations of User Query Variants	2201.07620	https://arxiv.org/abs/2201.07620v2	https://arxiv.org/pdf/2201.07620v2.pdf	https://github.com/irgroup/ecir2022-uqv-sim	true	true	false	none
https://paperswithcode.com/paper/the-imspoc-snapshot-imaging-spectrometer	Interferometer response characterization algorithm for multi-aperture Fabry-Perot imaging spectrometers	2303.14076	https://arxiv.org/abs/2303.14076v4	https://arxiv.org/pdf/2303.14076v4.pdf	https://github.com/danaroth83/irca	true	true	false	none
https://paperswithcode.com/paper/divnoising-diversity-denoising-with-fully	Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders	2006.06072	https://arxiv.org/abs/2006.06072v2	https://arxiv.org/pdf/2006.06072v2.pdf	https://github.com/IVRL/w2s	false	false	true	tf
https://paperswithcode.com/paper/joint-self-supervised-blind-denoising-and	Joint self-supervised blind denoising and noise estimation	2102.08023	https://arxiv.org/abs/2102.08023v1	https://arxiv.org/pdf/2102.08023v1.pdf	https://github.com/IVRL/w2s	false	false	true	tf
https://paperswithcode.com/paper/lessons-on-parameter-sharing-across-layers-in	Lessons on Parameter Sharing across Layers in Transformers	2104.06022	https://arxiv.org/abs/2104.06022v4	https://arxiv.org/pdf/2104.06022v4.pdf	https://github.com/takase/share_layer_params	true	true	true	pytorch
https://paperswithcode.com/paper/improving-blind-spot-denoising-for-microscopy	Improving Blind Spot Denoising for Microscopy	2008.08414	https://arxiv.org/abs/2008.08414v1	https://arxiv.org/pdf/2008.08414v1.pdf	https://github.com/IVRL/w2s	false	false	true	tf
https://paperswithcode.com/paper/energy-efficient-parking-analytics-system	Energy-Efficient Parking Analytics System using Deep Reinforcement Learning	2202.08973	https://arxiv.org/abs/2202.08973v2	https://arxiv.org/pdf/2202.08973v2.pdf	https://github.com/pittcps/rl-parking	true	true	true	pytorch
https://paperswithcode.com/paper/let-there-be-a-clock-on-the-beach-reducing	Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning	2110.01705	https://arxiv.org/abs/2110.01705v2	https://arxiv.org/pdf/2110.01705v2.pdf	https://github.com/furkanbiten/object-bias	true	true	true	pytorch
https://paperswithcode.com/paper/order-optimal-one-shot-distributed-learning	Order Optimal One-Shot Distributed Learning	1911.00731	https://arxiv.org/abs/1911.00731v1	https://arxiv.org/pdf/1911.00731v1.pdf	https://github.com/sabersalehk/MRE_C	false	false	true	none
https://paperswithcode.com/paper/few-shot-clustering-for-indoor-occupancy	Few shot clustering for indoor occupancy detection with extremely low-quality images from battery free cameras	2008.05654	https://arxiv.org/abs/2008.05654v1	https://arxiv.org/pdf/2008.05654v1.pdf	https://github.com/Homagn/Few_shot_clustering	true	false	true	pytorch
https://paperswithcode.com/paper/skew-gaussian-processes-for-classification	Skew Gaussian Processes for Classification	2005.12987	https://arxiv.org/abs/2005.12987v1	https://arxiv.org/pdf/2005.12987v1.pdf	https://github.com/benavoli/SkewGP	false	false	true	none
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/diff7/DARTS-devices	false	false	true	pytorch
https://paperswithcode.com/paper/a-non-parametric-proportional-risk-model-to	A non-parametric proportional risk model to assess a treatment effect in time-to-event data	2303.07479	https://arxiv.org/abs/2303.07479v1	https://arxiv.org/pdf/2303.07479v1.pdf	https://github.com/luciaameis/nppr-model-for-time-to-event-data	true	true	false	none
https://paperswithcode.com/paper/combining-learned-skills-and-reinforcement	Learning to combine primitive skills: A step towards versatile robotic manipulation	1908.00722	https://arxiv.org/abs/1908.00722v3	https://arxiv.org/pdf/1908.00722v3.pdf	https://github.com/rstrudel/rlbc	true	false	true	pytorch
https://paperswithcode.com/paper/learning-to-augment-synthetic-images-for	Learning to Augment Synthetic Images for Sim2Real Policy Transfer	1903.07740	https://arxiv.org/abs/1903.07740v2	https://arxiv.org/pdf/1903.07740v2.pdf	https://github.com/rstrudel/rlbc	true	false	true	pytorch
https://paperswithcode.com/paper/hypothesis-tests-for-structured-rank	Hypothesis tests for structured rank correlation matrices	2007.09738	https://arxiv.org/abs/2007.09738v2	https://arxiv.org/pdf/2007.09738v2.pdf	https://github.com/samperochkin/testing-tau	true	true	false	none
https://paperswithcode.com/paper/analysing-e-commerce-a-b-tests-with-dependent	Measuring e-Commerce Metric Changes in Online Experiments	2210.17187	https://arxiv.org/abs/2210.17187v2	https://arxiv.org/pdf/2210.17187v2.pdf	https://github.com/liuchbryan/oce-ecomm-abv-calculation	true	false	false	none
https://paperswithcode.com/paper/utilizing-automated-breast-cancer-detection	Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer	1905.10841	https://arxiv.org/abs/1905.10841v3	https://arxiv.org/pdf/1905.10841v3.pdf	https://github.com/SBU-BMI/quip_cancer_segmentation	false	false	true	pytorch
https://paperswithcode.com/paper/the-percolating-cluster-is-invisible-to-image	The percolating cluster is invisible to image recognition with deep learning	2303.15298	https://arxiv.org/abs/2303.15298v1	https://arxiv.org/pdf/2303.15298v1.pdf	https://github.com/DisQS/MachineLearning-Percolation	true	false	false	pytorch
https://paperswithcode.com/paper/semi-supervised-domain-generalizable-person	Semi-Supervised Domain Generalizable Person Re-Identification	2108.05045	https://arxiv.org/abs/2108.05045v2	https://arxiv.org/pdf/2108.05045v2.pdf	https://github.com/JDAI-CV/fast-reid	true	true	true	pytorch
https://paperswithcode.com/paper/exact-and-heuristic-algorithms-for-energy	Exact and Heuristic Algorithms for Energy-Efficient Scheduling	2203.14070	https://arxiv.org/abs/2203.14070v2	https://arxiv.org/pdf/2203.14070v2.pdf	https://github.com/orresearcher/phd-thesis	true	true	false	none
https://paperswithcode.com/paper/cost-sensitive-label-embedding-for-multi	Cost-Sensitive Label Embedding for Multi-Label Classification	1603.09048	http://arxiv.org/abs/1603.09048v5	http://arxiv.org/pdf/1603.09048v5.pdf	https://github.com/evantkchong/LEPAR	false	false	true	tf
https://paperswithcode.com/paper/fgcrec-fine-grained-geographical	FGCRec: Fine-Grained Geographical Characteristics Modeling for Point-of-Interest Recommendation	null	https://ieeexplore.ieee.org/document/9148797	http://www.suyijun.tech/papers/2020-ICC-FGCRec.pdf	https://github.com/YijunSu/ICC2020_FGCRec	false	false	false	none
https://paperswithcode.com/paper/grape-fast-and-scalable-graph-processing-and	GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding	2110.06196	https://arxiv.org/abs/2110.06196v3	https://arxiv.org/pdf/2110.06196v3.pdf	https://github.com/AnacletoLAB/ensmallen	false	false	false	none
https://paperswithcode.com/paper/lnemlc-label-network-embeddings-for-multi	LNEMLC: Label Network Embeddings for Multi-Label Classification	1812.02956	http://arxiv.org/abs/1812.02956v2	http://arxiv.org/pdf/1812.02956v2.pdf	https://github.com/evantkchong/LEPAR	false	false	true	tf
https://paperswithcode.com/paper/rethinking-of-pedestrian-attribute	Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method	2005.11909	https://arxiv.org/abs/2005.11909v2	https://arxiv.org/pdf/2005.11909v2.pdf	https://github.com/evantkchong/LEPAR	false	false	true	tf
https://paperswithcode.com/paper/deep-imbalanced-attribute-classification	Deep Imbalanced Attribute Classification using Visual Attention Aggregation	1807.03903	http://arxiv.org/abs/1807.03903v2	http://arxiv.org/pdf/1807.03903v2.pdf	https://github.com/evantkchong/LEPAR	false	false	true	tf
https://paperswithcode.com/paper/social-welfare-maximization-and-conformism	Maximizing Social Welfare and Agreement via Information Design in Linear-Quadratic-Gaussian Games	2102.13047	https://arxiv.org/abs/2102.13047v2	https://arxiv.org/pdf/2102.13047v2.pdf	https://github.com/furkansezer/Furkan-Sezer	false	false	true	none
https://paperswithcode.com/paper/effective-crowd-annotation-of-participants	Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports	null	https://aclanthology.org/2020.findings-emnlp.274	https://aclanthology.org/2020.findings-emnlp.274.pdf	https://github.com/markus-zlabinger/ssts	true	true	false	none
https://paperswithcode.com/paper/variational-inference-for-infinitely-deep	Variational Inference for Infinitely Deep Neural Networks	2209.10091	https://arxiv.org/abs/2209.10091v1	https://arxiv.org/pdf/2209.10091v1.pdf	https://github.com/anazaret/unbounded-depth-neural-networks	true	true	false	pytorch
https://paperswithcode.com/paper/extended-u-net-for-speaker-verification-in	Extended U-Net for Speaker Verification in Noisy Environments	2206.13044	https://arxiv.org/abs/2206.13044v1	https://arxiv.org/pdf/2206.13044v1.pdf	https://github.com/wngh1187/exu-net	true	true	false	pytorch
https://paperswithcode.com/paper/learning-spatiotemporal-features-with-3d	Learning Spatiotemporal Features with 3D Convolutional Networks	1412.0767	http://arxiv.org/abs/1412.0767v4	http://arxiv.org/pdf/1412.0767v4.pdf	https://github.com/facebookarchive/C3D	true	false	false	caffe2
https://paperswithcode.com/paper/desiderata-for-representation-learning-a	Desiderata for Representation Learning: A Causal Perspective	2109.03795	https://arxiv.org/abs/2109.03795v2	https://arxiv.org/pdf/2109.03795v2.pdf	https://github.com/yixinwang/representation-causal-public	false	false	true	pytorch
https://paperswithcode.com/paper/catch-a-waveform-learning-to-generate-audio	Catch-A-Waveform: Learning to Generate Audio from a Single Short Example	2106.06426	https://arxiv.org/abs/2106.06426v2	https://arxiv.org/pdf/2106.06426v2.pdf	https://github.com/galgreshler/Catch-A-Waveform	true	true	true	pytorch
https://paperswithcode.com/paper/mastering-atari-go-chess-and-shogi-by	Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model	1911.08265	https://arxiv.org/abs/1911.08265v2	https://arxiv.org/pdf/1911.08265v2.pdf	https://github.com/k-lombard/CS4641_Project	false	false	true	tf
https://paperswithcode.com/paper/medcare-advancing-medical-llms-through	MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation	2406.17484	https://arxiv.org/abs/2406.17484v3	https://arxiv.org/pdf/2406.17484v3.pdf	https://github.com/bluezeros/medcare	true	true	true	pytorch
https://paperswithcode.com/paper/icarl-incremental-classifier-and	iCaRL: Incremental Classifier and Representation Learning	1611.07725	http://arxiv.org/abs/1611.07725v2	http://arxiv.org/pdf/1611.07725v2.pdf	https://github.com/srebuffi/iCaRL	true	true	true	tf
https://paperswithcode.com/paper/analyzing-the-impact-of-meteorological	Analyzing the Impact of Meteorological Parameters on Rainfall Prediction	2110.11059	https://arxiv.org/abs/2110.11059v1	https://arxiv.org/pdf/2110.11059v1.pdf	https://github.com/sammyy092/impact-of-meteorological-parameters-on-rainfall	true	true	false	none
https://paperswithcode.com/paper/quantum-flatness-in-two-dimensional-cdt	Quantum Flatness in Two-Dimensional CDT Quantum Gravity	2110.11100	https://arxiv.org/abs/2110.11100v1	https://arxiv.org/pdf/2110.11100v1.pdf	https://github.com/jorenb/2d-cdt	true	true	false	none
https://paperswithcode.com/paper/deep-learning-approach-for-identification-of	Deep learning approach for identification of HII regions during reionization in 21-cm observations	2102.06713	https://arxiv.org/abs/2102.06713v2	https://arxiv.org/pdf/2102.06713v2.pdf	https://github.com/micbia/SegU-Net	true	true	true	tf
https://paperswithcode.com/paper/a-simple-framework-for-text-supervised	A Simple Framework for Text-Supervised Semantic Segmentation	null	http://openaccess.thecvf.com//content/CVPR2023/html/Yi_A_Simple_Framework_for_Text-Supervised_Semantic_Segmentation_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Yi_A_Simple_Framework_for_Text-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf	https://github.com/muyangyi/simseg	true	true	false	pytorch
https://paperswithcode.com/paper/raneus-ray-adaptive-neural-surface	RaNeuS: Ray-adaptive Neural Surface Reconstruction	2406.09801	https://arxiv.org/abs/2406.09801v1	https://arxiv.org/pdf/2406.09801v1.pdf	https://github.com/wangyida/ra-neus	true	true	false	pytorch
https://paperswithcode.com/paper/universality-of-critical-exponent-in-scale	Scaling properties of scale-free networks in degree-thresholding renormalization flows	2109.12309	https://arxiv.org/abs/2109.12309v2	https://arxiv.org/pdf/2109.12309v2.pdf	https://github.com/cdzqf/dtr	true	true	false	none
https://paperswithcode.com/paper/latentclr-a-contrastive-learning-approach-for	LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions	2104.00820	https://arxiv.org/abs/2104.00820v2	https://arxiv.org/pdf/2104.00820v2.pdf	https://github.com/gulperii/githubio	false	false	true	none
https://paperswithcode.com/paper/dense-passage-retrieval-for-open-domain	Dense Passage Retrieval for Open-Domain Question Answering	2004.04906	https://arxiv.org/abs/2004.04906v3	https://arxiv.org/pdf/2004.04906v3.pdf	https://github.com/alexlimh/DPR_MUF	false	false	true	pytorch
https://paperswithcode.com/paper/photo-pre-training-but-for-sketch	Photo Pre-Training, but for Sketch	null	http://openaccess.thecvf.com//content/CVPR2023/html/Li_Photo_Pre-Training_but_for_Sketch_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Photo_Pre-Training_but_for_Sketch_CVPR_2023_paper.pdf	https://github.com/keli-sketchx/photo-pre-training-but-for-sketch	true	true	false	none
https://paperswithcode.com/paper/orbit-a-unified-simulation-framework-for	Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments	2301.04195	https://arxiv.org/abs/2301.04195v2	https://arxiv.org/pdf/2301.04195v2.pdf	https://github.com/NVIDIA-Omniverse/Orbit	true	false	true	pytorch
https://paperswithcode.com/paper/a-real-time-deep-network-for-crowd-counting	A Real-Time Deep Network for Crowd Counting	2002.06515	https://arxiv.org/abs/2002.06515v1	https://arxiv.org/pdf/2002.06515v1.pdf	https://github.com/jplumail/people-counting	false	false	true	tf
https://paperswithcode.com/paper/contracttinker-llm-empowered-vulnerability	ContractTinker: LLM-Empowered Vulnerability Repair for Real-World Smart Contracts	2409.09661	https://arxiv.org/abs/2409.09661v1	https://arxiv.org/pdf/2409.09661v1.pdf	https://github.com/CheWang09/LLM4SMAPR	true	false	false	none
https://paperswithcode.com/paper/the-power-of-halometry	The Power of Halometry	2003.02264	https://arxiv.org/abs/2003.02264v1	https://arxiv.org/pdf/2003.02264v1.pdf	https://github.com/smsharma/neural-global-astrometry	false	false	true	pytorch
https://paperswithcode.com/paper/hci-papers-cite-hci-papers-increasingly-so	HCI Papers Cite HCI Papers, Increasingly So	2303.07539	https://arxiv.org/abs/2303.07539v2	https://arxiv.org/pdf/2303.07539v2.pdf	https://github.com/hotnany/x-index	true	true	false	none
https://paperswithcode.com/paper/deepsphere-a-graph-based-spherical-cnn-1	DeepSphere: a graph-based spherical CNN	2012.15000	https://arxiv.org/abs/2012.15000v1	https://arxiv.org/pdf/2012.15000v1.pdf	https://github.com/smsharma/neural-global-astrometry	false	false	true	pytorch
https://paperswithcode.com/paper/mining-for-dark-matter-substructure-inferring	Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning	1909.02005	https://arxiv.org/abs/1909.02005v2	https://arxiv.org/pdf/1909.02005v2.pdf	https://github.com/smsharma/neural-global-astrometry	false	false	true	pytorch
https://paperswithcode.com/paper/random-feature-stein-discrepancies	Random Feature Stein Discrepancies	1806.07788	https://arxiv.org/abs/1806.07788v5	https://arxiv.org/pdf/1806.07788v5.pdf	https://bitbucket.org/jhhuggins/random-feature-stein-discrepancies	true	true	true	none
https://paperswithcode.com/paper/exploring-simple-siamese-representation	Exploring Simple Siamese Representation Learning	2011.10566	https://arxiv.org/abs/2011.10566v1	https://arxiv.org/pdf/2011.10566v1.pdf	https://github.com/reza-safdari/simsiam-91.9-top1-acc-on-cifar10	false	false	true	pytorch
https://paperswithcode.com/paper/additive-margin-softmax-for-face-verification	Additive Margin Softmax for Face Verification	1801.05599	http://arxiv.org/abs/1801.05599v4	http://arxiv.org/pdf/1801.05599v4.pdf	https://github.com/dalisson/am_softmax	false	false	true	pytorch
https://paperswithcode.com/paper/a-siamese-cnn-for-image-steganalysis	A Siamese CNN for Image Steganalysis	null	https://ieeexplore.ieee.org/abstract/document/9153041	https://ieeexplore.ieee.org/abstract/document/9153041	https://github.com/albblgb/Deep-Steganalysis	false	false	false	pytorch
https://paperswithcode.com/paper/a-data-centric-framework-for-crystal	A data-centric framework for crystal structure identification in atomistic simulations using machine learning	2010.04815	https://arxiv.org/abs/2010.04815v5	https://arxiv.org/pdf/2010.04815v5.pdf	https://github.com/freitas-rodrigo/dc3	true	true	false	none
https://paperswithcode.com/paper/a-unified-framework-for-closed-form	A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes	2012.06846	https://arxiv.org/abs/2012.06846v2	https://arxiv.org/pdf/2012.06846v2.pdf	https://github.com/benavoli/SkewGP	true	true	true	none
https://paperswithcode.com/paper/domain-specific-bias-filtering-for-single	Domain-Specific Bias Filtering for Single Labeled Domain Generalization	2110.00726	https://arxiv.org/abs/2110.00726v3	https://arxiv.org/pdf/2110.00726v3.pdf	https://github.com/junkunyuan/dsbf	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.