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/deep-residual-learning-for-image-recognition	Deep Residual Learning for Image Recognition	1512.03385	http://arxiv.org/abs/1512.03385v1	http://arxiv.org/pdf/1512.03385v1.pdf	https://github.com/CryptoSalamander/pytorch_paper_implementation	false	false	true	pytorch
https://paperswithcode.com/paper/dynamic-modality-interaction-modeling-for	Dynamic Modality Interaction Modeling for Image-Text Retrieval	null	https://dl.acm.org/doi/abs/10.1145/3404835.3462829#d65226017e1	https://dl.acm.org/doi/pdf/10.1145/3404835.3462829	https://github.com/LgQu/DIME	false	false	false	pytorch
https://paperswithcode.com/paper/an-efficient-convolutional-neural-network-for	An Efficient Convolutional Neural Network for Coronary Heart Disease Prediction	1909.00489	https://arxiv.org/abs/1909.00489v2	https://arxiv.org/pdf/1909.00489v2.pdf	https://github.com/anik-UCB/CNN-CardioPrediction	false	false	true	tf
https://paperswithcode.com/paper/number-theoretic-transform-architecture	Number Theoretic Transform Architecture suitable to Lattice-based Fully-Homomorphic Encryption	null	https://ieeexplore.ieee.org/document/9516650	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9516650	https://github.com/rogpld/fhentt	false	false	false	none
https://paperswithcode.com/paper/an-infra-structure-for-performance-estimation	An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R	1412.0436	http://arxiv.org/abs/1412.0436v4	http://arxiv.org/pdf/1412.0436v4.pdf	https://github.com/ltorgo/performanceEstimation	true	true	false	none
https://paperswithcode.com/paper/thieves-on-sesame-street-model-extraction-of	Thieves on Sesame Street! Model Extraction of BERT-based APIs	1910.12366	https://arxiv.org/abs/1910.12366v3	https://arxiv.org/pdf/1910.12366v3.pdf	https://github.com/google-research/language/tree/master/language/bert_extraction	true	false	true	tf
https://paperswithcode.com/paper/annotating-and-characterizing-clinical	Annotating and Characterizing Clinical Sentences with Explicit Why-QA Cues	null	https://aclanthology.org/W19-1913	https://aclanthology.org/W19-1913.pdf	https://github.com/Jung-wei/ClinicalWhyQA	true	true	false	none
https://paperswithcode.com/paper/gradual-session-types	Gradual Session Types	1809.05649	http://arxiv.org/abs/1809.05649v1	http://arxiv.org/pdf/1809.05649v1.pdf	https://github.com/tdyydt/ggv-checker	false	false	true	none
https://paperswithcode.com/paper/the-blackbird-dataset-a-large-scale-dataset	The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight	1810.01987	http://arxiv.org/abs/1810.01987v1	http://arxiv.org/pdf/1810.01987v1.pdf	https://github.com/AgileDrones/Blackbird-Dataset	false	false	true	none
https://paperswithcode.com/paper/the-backpropagation-algorithm-implemented-on	The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware	2106.07030	https://arxiv.org/abs/2106.07030v2	https://arxiv.org/pdf/2106.07030v2.pdf	https://github.com/lanl/spikingBackprop	true	false	true	none
https://paperswithcode.com/paper/character-preserving-coherent-story	Character-Preserving Coherent Story Visualization	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2639_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620018.pdf	https://github.com/basiclab/CPCStoryVisualization-Pytorch	false	false	false	pytorch
https://paperswithcode.com/paper/improving-retrieval-based-question-answering	Improving Retrieval-Based Question Answering with Deep Inference Models	1812.02971	https://arxiv.org/abs/1812.02971v2	https://arxiv.org/pdf/1812.02971v2.pdf	https://github.com/SebiSebi/AI2-Reasoning-Challenge-ARC	true	true	true	none
https://paperswithcode.com/paper/transfer-learning-for-music-classification	Transfer learning for music classification and regression tasks	1703.09179	http://arxiv.org/abs/1703.09179v4	http://arxiv.org/pdf/1703.09179v4.pdf	https://github.com/DaCreasy/DLFinal	false	false	true	none
https://paperswithcode.com/paper/towards-a-relation-extraction-framework-for	Towards a relation extraction framework for cyber-security concepts	1504.04317	http://arxiv.org/abs/1504.04317v1	http://arxiv.org/pdf/1504.04317v1.pdf	https://github.com/ShashSec/SMTI_SA	false	false	true	none
https://paperswithcode.com/paper/automatic-labeling-for-entity-extraction-in	Automatic Labeling for Entity Extraction in Cyber Security	1308.4941	http://arxiv.org/abs/1308.4941v3	http://arxiv.org/pdf/1308.4941v3.pdf	https://github.com/ShashSec/SMTI_SA	false	false	true	none
https://paperswithcode.com/paper/sensitivity-analysis-on-chaotic-dynamical	Sensitivity analysis on chaotic dynamical system by Non-Intrusive Least Square Shadowing (NILSS)	1611.00880	http://arxiv.org/abs/1611.00880v8	http://arxiv.org/pdf/1611.00880v8.pdf	https://github.com/UriAceves/SiScSeminar	false	false	true	none
https://paperswithcode.com/paper/bayesian-probabilistic-numerical-methods-in	Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment	1707.06107	http://arxiv.org/abs/1707.06107v2	http://arxiv.org/pdf/1707.06107v2.pdf	https://github.com/jcockayne/hydrocyclone_code	true	true	true	none
https://paperswithcode.com/paper/searching-for-activation-functions	Searching for Activation Functions	1710.05941	http://arxiv.org/abs/1710.05941v2	http://arxiv.org/pdf/1710.05941v2.pdf	https://github.com/Neoanarika/Searching-for-activation-functions	false	false	true	tf
https://paperswithcode.com/paper/w-talc-weakly-supervised-temporal-activity	W-TALC: Weakly-supervised Temporal Activity Localization and Classification	1807.10418	http://arxiv.org/abs/1807.10418v3	http://arxiv.org/pdf/1807.10418v3.pdf	https://github.com/sujoyp/wtalc-pytorch	true	true	false	pytorch
https://paperswithcode.com/paper/correcting-contradictions	Correcting Contradictions	null	https://aclanthology.org/W17-7205	https://aclanthology.org/W17-7205.pdf	https://github.com/kkalouli/SICK-processing	true	true	false	none
https://paperswithcode.com/paper/learning-from-scale-invariant-examples-for	Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation	2007.14449	https://arxiv.org/abs/2007.14449v1	https://arxiv.org/pdf/2007.14449v1.pdf	https://github.com/MNaseerSubhani/LSE	false	false	true	pytorch
https://paperswithcode.com/paper/pseudoedgenet-nuclei-segmentation-only-with	PseudoEdgeNet: Nuclei Segmentation only with Point Annotations	1906.02924	https://arxiv.org/abs/1906.02924v2	https://arxiv.org/pdf/1906.02924v2.pdf	https://github.com/CJLee94/Point-Supervised-Segmentation	false	false	true	pytorch
https://paperswithcode.com/paper/teaching-machines-to-read-and-comprehend	Teaching Machines to Read and Comprehend	1506.03340	http://arxiv.org/abs/1506.03340v3	http://arxiv.org/pdf/1506.03340v3.pdf	https://github.com/thinkwee/DPP_CNN_Summarization	false	false	true	pytorch
https://paperswithcode.com/paper/exploring-recombination-for-efficient	Exploring Recombination for Efficient Decoding of Neural Machine Translation	1808.08482	http://arxiv.org/abs/1808.08482v2	http://arxiv.org/pdf/1808.08482v2.pdf	https://github.com/zzsfornlp/znmt-merge	true	true	false	none
https://paperswithcode.com/paper/where-are-the-blobs-counting-by-localization	Where are the Blobs: Counting by Localization with Point Supervision	1807.09856	http://arxiv.org/abs/1807.09856v1	http://arxiv.org/pdf/1807.09856v1.pdf	https://github.com/CJLee94/Point-Supervised-Segmentation	false	false	true	pytorch
https://paperswithcode.com/paper/backtranslation-feedback-improves-user	Backtranslation Feedback Improves User Confidence in MT, Not Quality	2104.05688	https://arxiv.org/abs/2104.05688v1	https://arxiv.org/pdf/2104.05688v1.pdf	https://github.com/zouharvi/ptakopet	true	true	false	none
https://paperswithcode.com/paper/learning-the-kernel-matrix-via-predictive-low	Learning the kernel matrix via predictive low-rank approximations	1601.04366	http://arxiv.org/abs/1601.04366v2	http://arxiv.org/pdf/1601.04366v2.pdf	https://github.com/mstrazar/mklaren	true	true	true	none
https://paperswithcode.com/paper/learning-a-dilated-residual-network-for-sar	Learning a Dilated Residual Network for SAR Image Despeckling	1709.02898	http://arxiv.org/abs/1709.02898v3	http://arxiv.org/pdf/1709.02898v3.pdf	https://github.com/qzhang95/SAR-DRN	false	false	true	pytorch
https://paperswithcode.com/paper/sensitive-information-tracking-in-commodity	Sensitive Information Tracking in Commodity IoT	1802.08307	http://arxiv.org/abs/1802.08307v1	http://arxiv.org/pdf/1802.08307v1.pdf	https://github.com/IoTBench/IoTBench-test-suite	false	false	true	none
https://paperswithcode.com/paper/learning-to-compose-neural-networks-for	Learning to Compose Neural Networks for Question Answering	1601.01705	http://arxiv.org/abs/1601.01705v4	http://arxiv.org/pdf/1601.01705v4.pdf	https://github.com/jacobandreas/nmn2	true	true	true	none
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/jonnor/datascience-master	false	false	true	none
https://paperswithcode.com/paper/receipt-refine-coarse-grained-independent	RECEIPT: REfine CoarsE-grained IndePendent Tasks for Parallel Tip decomposition of Bipartite Graphs	2010.08695	http://arxiv.org/abs/2010.08695v1	http://arxiv.org/pdf/2010.08695v1.pdf	https://github.com/kartiklakhotia/RECEIPT	true	true	true	none
https://paperswithcode.com/paper/named-entity-recognition-with-bidirectional	Named Entity Recognition with Bidirectional LSTM-CNNs	1511.08308	http://arxiv.org/abs/1511.08308v5	http://arxiv.org/pdf/1511.08308v5.pdf	https://github.com/osamadev/NER_Using_Spacy	false	false	true	none
https://paperswithcode.com/paper/discriminating-between-lexico-semantic	Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model	null	https://aclanthology.org/N18-2029	https://aclanthology.org/N18-2029.pdf	https://github.com/codogogo/stm	true	true	false	tf
https://paperswithcode.com/paper/compressed-sensing-in-astronomy	Compressed Sensing in Astronomy	0802.0131	http://arxiv.org/abs/0802.0131v1	http://arxiv.org/pdf/0802.0131v1.pdf	https://github.com/MartKl/CS_image_recovery_demo	false	false	true	none
https://paperswithcode.com/paper/byel-bootstrap-on-your-emotion-latent	BYEL : Bootstrap Your Emotion Latent	2207.10003	https://arxiv.org/abs/2207.10003v2	https://arxiv.org/pdf/2207.10003v2.pdf	https://github.com/rhtm02/Bootstrap-Your-Emotion-Latent	true	false	true	pytorch
https://paperswithcode.com/paper/spectral-normalization-for-generative	Spectral Normalization for Generative Adversarial Networks	1802.05957	http://arxiv.org/abs/1802.05957v1	http://arxiv.org/pdf/1802.05957v1.pdf	https://github.com/hinofafa/Self-Attention-HearthStone-GAN	false	false	true	pytorch
https://paperswithcode.com/paper/magnifiernet-towards-semantic-regularization	MagnifierNet: Towards Semantic Adversary and Fusion for Person Re-identification	2002.10979	https://arxiv.org/abs/2002.10979v4	https://arxiv.org/pdf/2002.10979v4.pdf	https://github.com/NIRVANALAN/magnifiernet_reid	false	false	false	pytorch
https://paperswithcode.com/paper/yolov3-an-incremental-improvement	YOLOv3: An Incremental Improvement	1804.02767	http://arxiv.org/abs/1804.02767v1	http://arxiv.org/pdf/1804.02767v1.pdf	https://github.com/jessychen1016/yolov3Onpytorch	false	false	true	pytorch
https://paperswithcode.com/paper/text-detection-and-recognition-in-the-wild-a	Text Detection and Recognition in the Wild: A Review	2006.04305	https://arxiv.org/abs/2006.04305v2	https://arxiv.org/pdf/2006.04305v2.pdf	https://github.com/shengyp/fake_news	false	false	true	none
https://paperswithcode.com/paper/an-empirical-analysis-of-the-efficacy-of	An Empirical Analysis of the Efficacy of Different Sampling Techniques for Imbalanced Classification	2208.11852	https://arxiv.org/abs/2208.11852v1	https://arxiv.org/pdf/2208.11852v1.pdf	https://github.com/newaz-aa/empirical_analysis_of_sampling_techniques	true	true	false	none
https://paperswithcode.com/paper/leveraging-high-dimensional-side-information	Leveraging High-Dimensional Side Information for Top-N Recommendation	1702.01516	http://arxiv.org/abs/1702.01516v2	http://arxiv.org/pdf/1702.01516v2.pdf	https://github.com/IvanVigor/Leveraging-High-Dimensional-Side-Information-for-Top-N-Recommendation	false	false	true	none
https://paperswithcode.com/paper/a-fast-and-well-conditioned-spectral-method	A fast and well-conditioned spectral method for singular integral equations	1507.00596	http://arxiv.org/abs/1507.00596v2	http://arxiv.org/pdf/1507.00596v2.pdf	https://github.com/UnofficialJuliaMirror/SingularIntegralEquations.jl-e094c991-5a90-5477-8896-c1e4c9552a1a	false	false	true	none
https://paperswithcode.com/paper/edgecnn-convolutional-neural-network	EdgeCNN: Convolutional Neural Network Classification Model with small inputs for Edge Computing	1909.13522	https://arxiv.org/abs/1909.13522v1	https://arxiv.org/pdf/1909.13522v1.pdf	https://github.com/yangshunzhi1994/EdgeCNN	true	true	false	pytorch
https://paperswithcode.com/paper/bayesian-latent-structure-discovery-from	Bayesian latent structure discovery from multi-neuron recordings	1610.08465	http://arxiv.org/abs/1610.08465v1	http://arxiv.org/pdf/1610.08465v1.pdf	https://github.com/slinderman/pypolyagamma	false	false	true	none
https://paperswithcode.com/paper/machine-learning-as-a-service-for-hep	Machine Learning as a Service for HEP	1811.04492	http://arxiv.org/abs/1811.04492v2	http://arxiv.org/pdf/1811.04492v2.pdf	https://github.com/vkuznet/MLaaS4HEP	true	true	true	pytorch
https://paperswithcode.com/paper/joint-entity-recognition-and-relation	Joint entity recognition and relation extraction as a multi-head selection problem	1804.07847	http://arxiv.org/abs/1804.07847v3	http://arxiv.org/pdf/1804.07847v3.pdf	https://github.com/Sanjithae/Joint_NER_RE	false	false	true	tf
https://paperswithcode.com/paper/mastering-chess-and-shogi-by-self-play-with-a	Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm	1712.01815	http://arxiv.org/abs/1712.01815v1	http://arxiv.org/pdf/1712.01815v1.pdf	https://github.com/QueensGambit/CrazyAra	false	false	true	mxnet
https://paperswithcode.com/paper/finding-structural-knowledge-in-multimodal	Finding Structural Knowledge in Multimodal-BERT	2203.09306	https://arxiv.org/abs/2203.09306v1	https://arxiv.org/pdf/2203.09306v1.pdf	https://github.com/vsjmilewski/multimodal-probes	true	true	true	pytorch
https://paperswithcode.com/paper/discriminative-region-proposal-adversarial	Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation	1711.09554	http://arxiv.org/abs/1711.09554v3	http://arxiv.org/pdf/1711.09554v3.pdf	https://github.com/godisboy/DRPAN	true	true	false	pytorch
https://paperswithcode.com/paper/holist-an-environment-for-machine-learning-of	HOList: An Environment for Machine Learning of Higher-Order Theorem Proving	1904.03241	https://arxiv.org/abs/1904.03241v3	https://arxiv.org/pdf/1904.03241v3.pdf	https://github.com/tensorflow/deepmath/tree/master/deepmath/deephol	false	false	false	tf
https://paperswithcode.com/paper/demorphy-german-language-morphological	DEMorphy, German Language Morphological Analyzer	1803.00902	http://arxiv.org/abs/1803.00902v1	http://arxiv.org/pdf/1803.00902v1.pdf	https://github.com/DuyguA/german-morph-dictionaries	true	true	true	none
https://paperswithcode.com/paper/fine-tuning-cnn-image-retrieval-with-no-human	Fine-tuning CNN Image Retrieval with No Human Annotation	1711.02512	http://arxiv.org/abs/1711.02512v2	http://arxiv.org/pdf/1711.02512v2.pdf	https://github.com/filipradenovic/cnnimageretrieval-pytorch	true	false	true	pytorch
https://paperswithcode.com/paper/pix2code-generating-code-from-a-graphical	pix2code: Generating Code from a Graphical User Interface Screenshot	1705.07962	http://arxiv.org/abs/1705.07962v2	http://arxiv.org/pdf/1705.07962v2.pdf	https://github.com/dashweta/MachineLearningPython	false	false	true	tf
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/bailvwangzi/repulsion_loss_ssd	false	false	true	pytorch
https://paperswithcode.com/paper/decaf-a-deep-convolutional-activation-feature	DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition	1310.1531	http://arxiv.org/abs/1310.1531v1	http://arxiv.org/pdf/1310.1531v1.pdf	https://github.com/UCB-ICSI-Vision-Group/decaf-release	false	false	true	none
https://paperswithcode.com/paper/how-energy-efficient-can-a-wireless	How Energy-Efficient Can a Wireless Communication System Become?	1812.01688	https://arxiv.org/abs/1812.01688v2	https://arxiv.org/pdf/1812.01688v2.pdf	https://github.com/emilbjornson/how-energy-efficient	true	false	true	none
https://paperswithcode.com/paper/a-unified-pseudo-c_ell-framework	A unified pseudo-$C_\ell$ framework	1809.09603	http://arxiv.org/abs/1809.09603v2	http://arxiv.org/pdf/1809.09603v2.pdf	https://github.com/andluizsouza/aps_namaster	false	false	true	none
https://paperswithcode.com/paper/high-magnitude-earthquake-identification	High Magnitude Earthquake Identification Using an Anomaly Detection Approach on HR GNSS Data	2412.00264	https://arxiv.org/abs/2412.00264v1	https://arxiv.org/pdf/2412.00264v1.pdf	https://github.com/srivastavaresearchgroup/saipy	true	false	false	pytorch
https://paperswithcode.com/paper/introduction-to-the-r-package-tda	Introduction to the R package TDA	1411.1830	http://arxiv.org/abs/1411.1830v2	http://arxiv.org/pdf/1411.1830v2.pdf	https://github.com/stephenhky/PyTDA	false	false	true	none
https://paperswithcode.com/paper/unpaired-image-to-image-translation-using	Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks	1703.10593	https://arxiv.org/abs/1703.10593v7	https://arxiv.org/pdf/1703.10593v7.pdf	https://github.com/xhujoy/CycleGAN-tensorflow	false	false	true	tf
https://paperswithcode.com/paper/communication-algorithms-via-deep-learning	Communication Algorithms via Deep Learning	1805.09317	http://arxiv.org/abs/1805.09317v1	http://arxiv.org/pdf/1805.09317v1.pdf	https://github.com/yihanjiang/Sequential-RNN-Decoder	true	true	false	pytorch
https://paperswithcode.com/paper/learning-to-fuse-music-genres-with-generative	Learning to Fuse Music Genres with Generative Adversarial Dual Learning	1712.01456	http://arxiv.org/abs/1712.01456v1	http://arxiv.org/pdf/1712.01456v1.pdf	https://github.com/aquastar/fusion_gan	true	true	false	tf
https://paperswithcode.com/paper/the-importance-of-being-recurrent-for	The Importance of Being Recurrent for Modeling Hierarchical Structure	1803.03585	http://arxiv.org/abs/1803.03585v2	http://arxiv.org/pdf/1803.03585v2.pdf	https://github.com/ketranm/fan_vs_rnn	true	true	false	pytorch
https://paperswithcode.com/paper/relative-pairwise-relationship-constrained	Relative Pairwise Relationship Constrained Non-negative Matrix Factorisation	1803.02218	http://arxiv.org/abs/1803.02218v1	http://arxiv.org/pdf/1803.02218v1.pdf	https://github.com/shawn-jiang/RPRNMF	true	true	false	none
https://paperswithcode.com/paper/going-deeper-with-convolutions	Going Deeper with Convolutions	1409.4842	http://arxiv.org/abs/1409.4842v1	http://arxiv.org/pdf/1409.4842v1.pdf	https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop	false	false	true	none
https://paperswithcode.com/paper/densebox-unifying-landmark-localization-with	DenseBox: Unifying Landmark Localization with End to End Object Detection	1509.04874	http://arxiv.org/abs/1509.04874v3	http://arxiv.org/pdf/1509.04874v3.pdf	https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop	false	false	true	none
https://paperswithcode.com/paper/value-function-approximation-via-low-rank	Value function approximation via low-rank models	1509.00061	http://arxiv.org/abs/1509.00061v1	http://arxiv.org/pdf/1509.00061v1.pdf	https://github.com/haoyio/LowRankMDP	true	true	false	none
https://paperswithcode.com/paper/deep-residual-learning-for-image-recognition	Deep Residual Learning for Image Recognition	1512.03385	http://arxiv.org/abs/1512.03385v1	http://arxiv.org/pdf/1512.03385v1.pdf	https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop	false	false	true	none
https://paperswithcode.com/paper/collective-mind-part-ii-towards-performance	Collective Mind, Part II: Towards Performance- and Cost-Aware Software Engineering as a Natural Science	1506.06256	http://arxiv.org/abs/1506.06256v1	http://arxiv.org/pdf/1506.06256v1.pdf	https://github.com/ctuning/ck-mxnet	false	false	true	mxnet
https://paperswithcode.com/paper/learning-tasks-for-multitask-learning	Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU	1806.02878	http://arxiv.org/abs/1806.02878v1	http://arxiv.org/pdf/1806.02878v1.pdf	https://github.com/mit-ddig/multitask-patients	true	true	false	tf
https://paperswithcode.com/paper/multi-fidelity-gaussian-process-bandit	Multi-fidelity Gaussian Process Bandit Optimisation	1603.06288	http://arxiv.org/abs/1603.06288v4	http://arxiv.org/pdf/1603.06288v4.pdf	https://github.com/kirthevasank/mf-gp-ucb	true	true	false	none
https://paperswithcode.com/paper/deepcode-feedback-codes-via-deep-learning	Deepcode: Feedback Codes via Deep Learning	1807.00801	http://arxiv.org/abs/1807.00801v1	http://arxiv.org/pdf/1807.00801v1.pdf	https://github.com/hyejikim1/Deepcode	true	true	false	tf
https://paperswithcode.com/paper/augmentor-an-image-augmentation-library-for	Augmentor: An Image Augmentation Library for Machine Learning	1708.04680	http://arxiv.org/abs/1708.04680v1	http://arxiv.org/pdf/1708.04680v1.pdf	https://github.com/Rahul-Venugopal/Image-augmentation_1	false	false	true	none
https://paperswithcode.com/paper/using-filter-banks-in-convolutional-neural	Using Filter Banks in Convolutional Neural Networks for Texture Classification	1601.02919	http://arxiv.org/abs/1601.02919v5	http://arxiv.org/pdf/1601.02919v5.pdf	https://github.com/v-andrearczyk/caffe-TCNN	true	true	false	none
https://paperswithcode.com/paper/towards-building-a-knowledge-base-of-monetary	Towards Building a Knowledge Base of Monetary Transactions from a News Collection	1709.05743	http://arxiv.org/abs/1709.05743v1	http://arxiv.org/pdf/1709.05743v1.pdf	https://github.com/benetka/kbmt	true	true	false	none
https://paperswithcode.com/paper/location-dependency-in-video-prediction	Location Dependency in Video Prediction	1810.04937	http://arxiv.org/abs/1810.04937v2	http://arxiv.org/pdf/1810.04937v2.pdf	https://github.com/AIS-Bonn/LocDepVideoPrediction	true	true	false	none
https://paperswithcode.com/paper/cdgan-cyclic-discriminative-generative	CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation	2001.05489	https://arxiv.org/abs/2001.05489v2	https://arxiv.org/pdf/2001.05489v2.pdf	https://github.com/KishanKancharagunta/CDGAN	true	true	true	pytorch
https://paperswithcode.com/paper/feature-pyramid-networks-for-object-detection	Feature Pyramid Networks for Object Detection	1612.03144	http://arxiv.org/abs/1612.03144v2	http://arxiv.org/pdf/1612.03144v2.pdf	https://github.com/huan123/py-fatser-rcnn	false	false	true	tf
https://paperswithcode.com/paper/deep-hidden-physics-models-deep-learning-of	Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations	1801.06637	http://arxiv.org/abs/1801.06637v1	http://arxiv.org/pdf/1801.06637v1.pdf	https://github.com/maziarraissi/DeepHPMs	true	true	true	tf
https://paperswithcode.com/paper/checkerboard-artifact-free-sub-pixel	Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize	1707.02937	http://arxiv.org/abs/1707.02937v1	http://arxiv.org/pdf/1707.02937v1.pdf	https://github.com/kostyaev/ICNR	false	false	true	tf
https://paperswithcode.com/paper/generating-person-images-with-appearance	Generating Person Images with Appearance-aware Pose Stylizer	2007.09077	https://arxiv.org/abs/2007.09077v1	https://arxiv.org/pdf/2007.09077v1.pdf	https://github.com/siyuhuang/PoseStylizer	true	true	false	pytorch
https://paperswithcode.com/paper/cover-learning-covariate-specific-vector	CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions	1802.07839	http://arxiv.org/abs/1802.07839v2	http://arxiv.org/pdf/1802.07839v2.pdf	https://github.com/justinaL/tag	false	false	true	tf
https://paperswithcode.com/paper/on-the-effects-of-small-graph-perturbations	On the Effects of Small Graph Perturbations in the MaxCut Problem by QAOA	2408.15413	https://arxiv.org/abs/2408.15413v1	https://arxiv.org/pdf/2408.15413v1.pdf	https://github.com/nesyalab/papers_with_code	true	true	false	none
https://paperswithcode.com/paper/structured-receptive-fields-in-cnns	Structured Receptive Fields in CNNs	1605.02971	http://arxiv.org/abs/1605.02971v2	http://arxiv.org/pdf/1605.02971v2.pdf	https://github.com/jhjacobsen/RFNN	true	true	true	none
https://paperswithcode.com/paper/u-net-convolutional-networks-for-biomedical	U-Net: Convolutional Networks for Biomedical Image Segmentation	1505.04597	http://arxiv.org/abs/1505.04597v1	http://arxiv.org/pdf/1505.04597v1.pdf	https://github.com/work-mohit/UNet-from-scratch	false	false	true	tf
https://paperswithcode.com/paper/hilloc-lossless-image-compression-with-1	HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models	1912.09953	https://arxiv.org/abs/1912.09953v1	https://arxiv.org/pdf/1912.09953v1.pdf	https://github.com/hilloc-submission/hilloc	true	true	false	jax
https://paperswithcode.com/paper/keep-it-smpl-automatic-estimation-of-3d-human	Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image	1607.08128	http://arxiv.org/abs/1607.08128v1	http://arxiv.org/pdf/1607.08128v1.pdf	https://github.com/Jtoo/fitting_human_smpl_model	false	false	true	tf
https://paperswithcode.com/paper/fairsight-visual-analytics-for-fairness-in	FairSight: Visual Analytics for Fairness in Decision Making	1908.00176	https://arxiv.org/abs/1908.00176v2	https://arxiv.org/pdf/1908.00176v2.pdf	https://github.com/ayong8/FairSight	false	false	false	none
https://paperswithcode.com/paper/yolov3-an-incremental-improvement	YOLOv3: An Incremental Improvement	1804.02767	http://arxiv.org/abs/1804.02767v1	http://arxiv.org/pdf/1804.02767v1.pdf	https://github.com/qqwweee/keras-yolo3	false	false	false	tf
https://paperswithcode.com/paper/learning-speaker-embedding-from-text-to	Learning Speaker Embedding from Text-to-Speech	2010.11221	https://arxiv.org/abs/2010.11221v1	https://arxiv.org/pdf/2010.11221v1.pdf	https://github.com/JaejinCho/espnet_spkidtts	true	true	false	pytorch
https://paperswithcode.com/paper/synthesis-of-hybrid-automata-with-affine	Synthesis of Hybrid Automata with Affine Dynamics from Time-Series Data	2102.12734	https://arxiv.org/abs/2102.12734v1	https://arxiv.org/pdf/2102.12734v1.pdf	https://github.com/HySynth/HySynth	true	true	false	none
https://paperswithcode.com/paper/convolutional-neural-networks-for-sentence	Convolutional Neural Networks for Sentence Classification	1408.5882	http://arxiv.org/abs/1408.5882v2	http://arxiv.org/pdf/1408.5882v2.pdf	https://github.com/andrewfwalters/w266-final-project	false	false	true	pytorch
https://paperswithcode.com/paper/did-i-do-that-blame-as-a-means-to-identify	Did I do that? Blame as a means to identify controlled effects in reinforcement learning	2106.00266	https://arxiv.org/abs/2106.00266v3	https://arxiv.org/pdf/2106.00266v3.pdf	https://github.com/BY571/CEN-Network	false	false	true	pytorch
https://paperswithcode.com/paper/automated-deep-abstractions-for-stochastic	Automated Deep Abstractions for Stochastic Chemical Reaction Networks	2002.01889	https://arxiv.org/abs/2002.01889v1	https://arxiv.org/pdf/2002.01889v1.pdf	https://github.com/dennerepin/StochNetV2	false	false	true	tf
https://paperswithcode.com/paper/a-theoretically-grounded-application-of	A Theoretically Grounded Application of Dropout in Recurrent Neural Networks	1512.05287	http://arxiv.org/abs/1512.05287v5	http://arxiv.org/pdf/1512.05287v5.pdf	https://github.com/abdelrahmansaud/vLSTM	false	false	true	pytorch
https://paperswithcode.com/paper/anisotropic-osmosis-filtering-for-shadow	Anisotropic osmosis filtering for shadow removal in images	1809.06298	http://arxiv.org/abs/1809.06298v1	http://arxiv.org/pdf/1809.06298v1.pdf	https://github.com/simoneparisotto/Anisotropic-osmosis-filter	false	false	true	none
https://paperswithcode.com/paper/from-softmax-to-sparsemax-a-sparse-model-of	From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification	1602.02068	http://arxiv.org/abs/1602.02068v2	http://arxiv.org/pdf/1602.02068v2.pdf	https://github.com/vene/sparse-structured-attention	false	false	true	pytorch
https://paperswithcode.com/paper/fully-convolutional-networks-for-semantic-1	Fully Convolutional Networks for Semantic Segmentation	1411.4038	http://arxiv.org/abs/1411.4038v2	http://arxiv.org/pdf/1411.4038v2.pdf	https://github.com/GodPater/model_fcn	false	false	true	tf
https://paperswithcode.com/paper/learning-deep-features-for-discriminative	Learning Deep Features for Discriminative Localization	1512.04150	http://arxiv.org/abs/1512.04150v1	http://arxiv.org/pdf/1512.04150v1.pdf	https://github.com/schizop/SA	false	false	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.

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.