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/dynamic-dual-attentive-aggregation-learning | Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification | 2007.09314 | https://arxiv.org/abs/2007.09314v1 | https://arxiv.org/pdf/2007.09314v1.pdf | https://github.com/mangye16/DDAG | true | true | true | pytorch |
https://paperswithcode.com/paper/playing-chess-with-limited-look-ahead | Playing Chess with Limited Look Ahead | 2007.02130 | https://arxiv.org/abs/2007.02130v1 | https://arxiv.org/pdf/2007.02130v1.pdf | https://github.com/ArmanMaesumi/LimitedLookAheadChess | true | true | true | tf |
https://paperswithcode.com/paper/a-neural-algorithm-of-artistic-style | A Neural Algorithm of Artistic Style | 1508.06576 | http://arxiv.org/abs/1508.06576v2 | http://arxiv.org/pdf/1508.06576v2.pdf | https://github.com/Jitensid/Neural-Style-Transfer | false | false | true | tf |
https://paperswithcode.com/paper/graph-neural-network-for-traffic-forecasting | Graph Neural Network for Traffic Forecasting: A Survey | 2101.11174 | https://arxiv.org/abs/2101.11174v4 | https://arxiv.org/pdf/2101.11174v4.pdf | https://github.com/zhiyongc/Seattle-Loop-Data | true | true | false | none |
https://paperswithcode.com/paper/additive-noise-annealing-and-approximation | Additive Noise Annealing and Approximation Properties of Quantized Neural Networks | 1905.10452 | https://arxiv.org/abs/1905.10452v1 | https://arxiv.org/pdf/1905.10452v1.pdf | https://github.com/spallanzanimatteo/QuantLab | true | true | true | pytorch |
https://paperswithcode.com/paper/geometric-anomaly-detection-in-data | Geometric anomaly detection in data | 1908.09397 | https://arxiv.org/abs/1908.09397v1 | https://arxiv.org/pdf/1908.09397v1.pdf | https://github.com/stolzbernadette/Geometric-Anomalies | false | false | true | none |
https://paperswithcode.com/paper/fast-and-accurate-model-scaling | Fast and Accurate Model Scaling | 2103.06877 | https://arxiv.org/abs/2103.06877v1 | https://arxiv.org/pdf/2103.06877v1.pdf | https://github.com/tuggeluk/pycls | false | false | true | pytorch |
https://paperswithcode.com/paper/exact-hard-monotonic-attention-for-character | Exact Hard Monotonic Attention for Character-Level Transduction | 1905.06319 | https://arxiv.org/abs/1905.06319v3 | https://arxiv.org/pdf/1905.06319v3.pdf | https://github.com/AssafSinger94/sigmorphon-2020-inflection | false | false | true | pytorch |
https://paperswithcode.com/paper/solving-large-scale-structure-in-ten-easy | Solving Large Scale Structure in Ten Easy Steps with COLA | 1301.0322 | https://arxiv.org/abs/1301.0322v1 | https://arxiv.org/pdf/1301.0322v1.pdf | https://github.com/HAWinther/MG-PICOLA-PUBLIC | false | false | true | none |
https://paperswithcode.com/paper/hierarchical-multi-head-attentive-network-for | Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection | 2102.02680 | https://arxiv.org/abs/2102.02680v1 | https://arxiv.org/pdf/2102.02680v1.pdf | https://github.com/nguyenvo09/EACL2021 | true | true | false | 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/PingoLH/CenterNet-HarDNet | false | false | true | pytorch |
https://paperswithcode.com/paper/hardnet-a-low-memory-traffic-network | HarDNet: A Low Memory Traffic Network | 1909.00948 | https://arxiv.org/abs/1909.00948v1 | https://arxiv.org/pdf/1909.00948v1.pdf | https://github.com/PingoLH/CenterNet-HarDNet | false | false | true | pytorch |
https://paperswithcode.com/paper/cross-domain-adaptation-of-spoken-language | Cross-Domain Adaptation of Spoken Language Identification for Related Languages: The Curious Case of Slavic Languages | 2008.00545 | https://arxiv.org/abs/2008.00545v2 | https://arxiv.org/pdf/2008.00545v2.pdf | https://github.com/uds-lsv/da-lang-id | true | true | false | pytorch |
https://paperswithcode.com/paper/amortized-synthesis-of-constrained | Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate | 2106.09019 | https://arxiv.org/abs/2106.09019v2 | https://arxiv.org/pdf/2106.09019v2.pdf | https://github.com/xingyuansun/amorsyn | true | true | true | pytorch |
https://paperswithcode.com/paper/the-liver-tumor-segmentation-benchmark-lits | The Liver Tumor Segmentation Benchmark (LiTS) | 1901.04056 | https://arxiv.org/abs/1901.04056v2 | https://arxiv.org/pdf/1901.04056v2.pdf | https://github.com/zz10001/LITS2017-main1 | false | false | false | pytorch |
https://paperswithcode.com/paper/weakly-supervised-generative-network-for | Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses | 2008.05770 | https://arxiv.org/abs/2008.05770v1 | https://arxiv.org/pdf/2008.05770v1.pdf | https://github.com/chaneyddtt/weakly-supervised-3d-pose-generator | true | true | false | tf |
https://paperswithcode.com/paper/iterative-surrogate-model-optimization-ismo | Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks | 2008.05730 | https://arxiv.org/abs/2008.05730v1 | https://arxiv.org/pdf/2008.05730v1.pdf | https://github.com/kjetil-lye/iterative_surrogate_optimization | true | true | true | none |
https://paperswithcode.com/paper/lac-lstm-autoencoder-with-community-for | LAC : LSTM AUTOENCODER with Community for Insider Threat Detection | 2008.05646 | https://arxiv.org/abs/2008.05646v1 | https://arxiv.org/pdf/2008.05646v1.pdf | https://github.com/smlab-niser/LAC | true | true | false | none |
https://paperswithcode.com/paper/composition-based-crystal-materials-symmetry | Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors | 2105.07303 | https://arxiv.org/abs/2105.07303v1 | https://arxiv.org/pdf/2105.07303v1.pdf | https://github.com/usccolumbia/SG_predict | true | false | false | none |
https://paperswithcode.com/paper/calculating-elements-of-matrix-functions | Calculating elements of matrix functions using divided differences | 2107.14124 | https://arxiv.org/abs/2107.14124v2 | https://arxiv.org/pdf/2107.14124v2.pdf | https://github.com/LevBarash/MatrixFunctions | true | true | false | none |
https://paperswithcode.com/paper/evaluating-protein-transfer-learning-with | Evaluating Protein Transfer Learning with TAPE | 1906.08230 | https://arxiv.org/abs/1906.08230v1 | https://arxiv.org/pdf/1906.08230v1.pdf | https://github.com/googleinterns/protein-embedding-retrieval | false | false | true | jax |
https://paperswithcode.com/paper/contextual-lensing-of-universal-sentence | Contextual Lensing of Universal Sentence Representations | 2002.08866 | https://arxiv.org/abs/2002.08866v1 | https://arxiv.org/pdf/2002.08866v1.pdf | https://github.com/googleinterns/protein-embedding-retrieval | false | false | true | jax |
https://paperswithcode.com/paper/fixed-length-protein-embeddings-using | Fixed-Length Protein Embeddings using Contextual Lenses | 2010.15065 | https://arxiv.org/abs/2010.15065v1 | https://arxiv.org/pdf/2010.15065v1.pdf | https://github.com/googleinterns/protein-embedding-retrieval | true | true | false | jax |
https://paperswithcode.com/paper/beyond-outlier-detection-outlier | Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network | null | https://dl.acm.org/doi/10.1145/3442381.3449868 | https://dl.acm.org/doi/10.1145/3442381.3449868 | https://github.com/xuhongzuo/outlier-interpretation | false | false | false | pytorch |
https://paperswithcode.com/paper/spatialflow-bridging-all-tasks-for-panoptic | SpatialFlow: Bridging All Tasks for Panoptic Segmentation | 1910.08787 | https://arxiv.org/abs/1910.08787v3 | https://arxiv.org/pdf/1910.08787v3.pdf | https://github.com/chensnathan/SpatialFlow | true | true | true | pytorch |
https://paperswithcode.com/paper/heuristics-for-inequality-minimization-in | Heuristics for Inequality minimization in PageRank values | 2310.18537 | https://arxiv.org/abs/2310.18537v2 | https://arxiv.org/pdf/2310.18537v2.pdf | https://github.com/puzzlef/pagerank-minimize-inequality | true | true | false | none |
https://paperswithcode.com/paper/predicting-radial-velocity-jitter-induced-by | Predicting radial-velocity jitter induced by stellar oscillations based on Kepler data | 1807.00096 | http://arxiv.org/abs/1807.00096v1 | http://arxiv.org/pdf/1807.00096v1.pdf | https://github.com/Jieyu126/Jitter | true | true | false | none |
https://paperswithcode.com/paper/echo-syncnet-self-supervised-cardiac-view | Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography | 2102.02287 | https://arxiv.org/abs/2102.02287v1 | https://arxiv.org/pdf/2102.02287v1.pdf | https://github.com/fatemehtd/Echo-SyncNet | true | true | false | tf |
https://paperswithcode.com/paper/trifinger-an-open-source-robot-for-learning | TriFinger: An Open-Source Robot for Learning Dexterity | 2008.03596 | https://arxiv.org/abs/2008.03596v2 | https://arxiv.org/pdf/2008.03596v2.pdf | https://github.com/open-dynamic-robot-initiative/trifinger_simulation | false | false | true | none |
https://paperswithcode.com/paper/constructing-narrative-event-evolutionary | Constructing Narrative Event Evolutionary Graph for Script Event Prediction | 1805.05081 | http://arxiv.org/abs/1805.05081v2 | http://arxiv.org/pdf/1805.05081v2.pdf | https://github.com/eecrazy/ConstructingNEEG_IJCAI_2018 | true | true | true | pytorch |
https://paperswithcode.com/paper/kaleidoscope-an-efficient-learnable-1 | Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps | 2012.14966 | https://arxiv.org/abs/2012.14966v2 | https://arxiv.org/pdf/2012.14966v2.pdf | https://github.com/HazyResearch/learning-circuits | true | true | false | pytorch |
https://paperswithcode.com/paper/guidelines-for-responsible-and-human-centered | Proposed Guidelines for the Responsible Use of Explainable Machine Learning | 1906.03533 | https://arxiv.org/abs/1906.03533v3 | https://arxiv.org/pdf/1906.03533v3.pdf | https://github.com/jphall663/hc_ml | false | false | true | none |
https://paperswithcode.com/paper/srda-generating-instance-segmentation | SRDA: Generating Instance Segmentation Annotation Via Scanning, Reasoning And Domain Adaptation | 1801.08839 | http://arxiv.org/abs/1801.08839v3 | http://arxiv.org/pdf/1801.08839v3.pdf | https://github.com/DirtyHarryLYL/SRDA-ECCV2018 | true | false | false | 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/meiqiguo/iccv2021-atypicalitydetection | false | false | true | pytorch |
https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for | EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks | 1905.11946 | https://arxiv.org/abs/1905.11946v5 | https://arxiv.org/pdf/1905.11946v5.pdf | https://github.com/darya-baranovskaya/keyword_spotting | false | false | true | pytorch |
https://paperswithcode.com/paper/language-id-in-the-wild-unexpected-challenges | Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus | 2010.14571 | https://arxiv.org/abs/2010.14571v2 | https://arxiv.org/pdf/2010.14571v2.pdf | https://github.com/google-research-datasets/TF-IDF-IIF-top100-wordlists | true | true | true | tf |
https://paperswithcode.com/paper/ecapa-tdnn-for-multi-speaker-text-to-speech | ECAPA-TDNN for Multi-speaker Text-to-speech Synthesis | 2203.10473 | https://arxiv.org/abs/2203.10473v2 | https://arxiv.org/pdf/2203.10473v2.pdf | https://github.com/2023-MindSpore-1/ms-code-50 | false | false | false | mindspore |
https://paperswithcode.com/paper/efficient-clustering-based-on-a-unified-view | Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut | null | http://proceedings.neurips.cc/paper/2020/hash/aa108f56a10e75c1f20f27723ecac85f-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/aa108f56a10e75c1f20f27723ecac85f-Paper.pdf | https://github.com/ShenfeiPei/KSUMS | true | true | false | none |
https://paperswithcode.com/paper/r-markdown-integrating-a-reproducible | R Markdown: Integrating A Reproducible Analysis Tool into Introductory Statistics | 1402.1894 | https://arxiv.org/abs/1402.1894v1 | https://arxiv.org/pdf/1402.1894v1.pdf | https://github.com/liibre/curso | false | false | true | none |
https://paperswithcode.com/paper/learning-to-adapt-structured-output-space-for | Learning to Adapt Structured Output Space for Semantic Segmentation | 1802.10349 | https://arxiv.org/abs/1802.10349v3 | https://arxiv.org/pdf/1802.10349v3.pdf | https://github.com/buriedms/AdaptSegNet-Paddle | false | false | true | paddle |
https://paperswithcode.com/paper/identity-aware-multi-sentence-video | Identity-Aware Multi-Sentence Video Description | 2008.09791 | https://arxiv.org/abs/2008.09791v1 | https://arxiv.org/pdf/2008.09791v1.pdf | https://github.com/jamespark3922/lsmdc-fillin | true | true | true | pytorch |
https://paperswithcode.com/paper/lowfer-low-rank-bilinear-pooling-for-link | LowFER: Low-rank Bilinear Pooling for Link Prediction | 2008.10858 | https://arxiv.org/abs/2008.10858v1 | https://arxiv.org/pdf/2008.10858v1.pdf | https://github.com/suamin/LowFER | true | true | false | pytorch |
https://paperswithcode.com/paper/revphiseg-a-memory-efficient-neural-network | RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation | 2008.06999 | https://arxiv.org/abs/2008.06999v2 | https://arxiv.org/pdf/2008.06999v2.pdf | https://github.com/gigantenbein/UNet-Zoo | true | true | true | pytorch |
https://paperswithcode.com/paper/learning-to-reason-in-round-based-games-multi | Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person Shooters | 2008.05131 | https://arxiv.org/abs/2008.05131v1 | https://arxiv.org/pdf/2008.05131v1.pdf | https://github.com/derenlei/CS_Net | true | true | true | pytorch |
https://paperswithcode.com/paper/covid-19-data-analysis-and-forecasting | COVID-19 Data Analysis and Forecasting: Algeria and the World | 2007.09755 | https://arxiv.org/abs/2007.09755v2 | https://arxiv.org/pdf/2007.09755v2.pdf | https://github.com/SamBelkacem/COVID19-Algeria-and-World-Dataset | true | true | false | none |
https://paperswithcode.com/paper/unsupervised-learning-of-particle-image | Unsupervised Learning of Particle Image Velocimetry | 2007.14487 | https://arxiv.org/abs/2007.14487v1 | https://arxiv.org/pdf/2007.14487v1.pdf | https://github.com/erizmr/UnLiteFlowNet-PIV | true | true | true | pytorch |
https://paperswithcode.com/paper/robust-ego-and-object-6-dof-motion-estimation | Robust Ego and Object 6-DoF Motion Estimation and Tracking | 2007.13993 | https://arxiv.org/abs/2007.13993v1 | https://arxiv.org/pdf/2007.13993v1.pdf | https://github.com/halajun/multimot_track | true | true | true | none |
https://paperswithcode.com/paper/xinggan-for-person-image-generation | XingGAN for Person Image Generation | 2007.09278 | https://arxiv.org/abs/2007.09278v1 | https://arxiv.org/pdf/2007.09278v1.pdf | https://github.com/Ha0Tang/XingGAN | true | true | true | pytorch |
https://paperswithcode.com/paper/learning-to-match-distributions-for-domain | Learning to Match Distributions for Domain Adaptation | 2007.10791 | https://arxiv.org/abs/2007.10791v3 | https://arxiv.org/pdf/2007.10791v3.pdf | https://github.com/jindongwang/transferlearning | true | true | true | pytorch |
https://paperswithcode.com/paper/shape-prior-deformation-for-categorical-6d | Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation | 2007.08454 | https://arxiv.org/abs/2007.08454v1 | https://arxiv.org/pdf/2007.08454v1.pdf | https://github.com/mentian/object-deformnet | true | true | true | pytorch |
https://paperswithcode.com/paper/mixture-complexity-and-its-application-to | Mixture Complexity and Its Application to Gradual Clustering Change Detection | 2007.07467 | https://arxiv.org/abs/2007.07467v1 | https://arxiv.org/pdf/2007.07467v1.pdf | https://github.com/ShunkiKyoya/MixtureComplexity | true | true | false | none |
https://paperswithcode.com/paper/semi-siamese-training-for-shallow-face | Semi-Siamese Training for Shallow Face Learning | 2007.08398 | https://arxiv.org/abs/2007.08398v1 | https://arxiv.org/pdf/2007.08398v1.pdf | https://github.com/dituu/Semi-Siamese-Training | true | true | true | pytorch |
https://paperswithcode.com/paper/patch-wise-attack-for-fooling-deep-neural | Patch-wise Attack for Fooling Deep Neural Network | 2007.06765 | https://arxiv.org/abs/2007.06765v3 | https://arxiv.org/pdf/2007.06765v3.pdf | https://github.com/qilong-zhang/Patch-wise-iterative-attack | true | true | true | tf |
https://paperswithcode.com/paper/template-based-question-generation-from | Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering | 2004.11892 | https://arxiv.org/abs/2004.11892v1 | https://arxiv.org/pdf/2004.11892v1.pdf | https://github.com/awslabs/unsupervised-qa | true | true | true | none |
https://paperswithcode.com/paper/certifying-joint-adversarial-robustness-for | Certifying Joint Adversarial Robustness for Model Ensembles | 2004.10250 | https://arxiv.org/abs/2004.10250v1 | https://arxiv.org/pdf/2004.10250v1.pdf | https://github.com/jonas-maj/ensemble-adversarial-robustness | true | true | true | pytorch |
https://paperswithcode.com/paper/lrtd-long-range-temporal-dependency-based | LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow Recognition | 2004.09845 | https://arxiv.org/abs/2004.09845v2 | https://arxiv.org/pdf/2004.09845v2.pdf | https://github.com/xmichelleshihx/AL-LRTD | true | true | true | pytorch |
https://paperswithcode.com/paper/simalign-high-quality-word-alignments-without | SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings | 2004.08728 | https://arxiv.org/abs/2004.08728v4 | https://arxiv.org/pdf/2004.08728v4.pdf | https://github.com/masoudjs/simalign | true | true | true | pytorch |
https://paperswithcode.com/paper/a-novel-cnn-based-method-for-accurate-ship | A Novel CNN-based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box | 2004.07124 | https://arxiv.org/abs/2004.07124v2 | https://arxiv.org/pdf/2004.07124v2.pdf | https://github.com/lilinhao/ShipDetection | true | true | false | none |
https://paperswithcode.com/paper/geomstats-a-python-package-for-riemannian-2 | Geomstats: A Python Package for Riemannian Geometry in Machine Learning | 2004.04667 | https://arxiv.org/abs/2004.04667v1 | https://arxiv.org/pdf/2004.04667v1.pdf | https://github.com/geomstats/geomstats | true | true | true | pytorch |
https://paperswithcode.com/paper/a-systematic-analysis-of-morphological | A Systematic Analysis of Morphological Content in BERT Models for Multiple Languages | 2004.03032 | https://arxiv.org/abs/2004.03032v1 | https://arxiv.org/pdf/2004.03032v1.pdf | https://github.com/danedmiston/morphology_classifiers | true | true | false | pytorch |
https://paperswithcode.com/paper/self-supervised-viewpoint-learning-from-image | Self-Supervised Viewpoint Learning From Image Collections | 2004.01793 | https://arxiv.org/abs/2004.01793v1 | https://arxiv.org/pdf/2004.01793v1.pdf | https://github.com/NVlabs/SSV | true | true | true | pytorch |
https://paperswithcode.com/paper/disentangling-and-unifying-graph-convolutions | Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition | 2003.14111 | https://arxiv.org/abs/2003.14111v2 | https://arxiv.org/pdf/2003.14111v2.pdf | https://github.com/kenziyuliu/ms-g3d | true | true | true | pytorch |
https://paperswithcode.com/paper/real-time-detection-of-dictionary-dga-network | Real-Time Detection of Dictionary DGA Network Traffic using Deep Learning | 2003.12805 | https://arxiv.org/abs/2003.12805v1 | https://arxiv.org/pdf/2003.12805v1.pdf | https://github.com/jinxmirror13/bilbo-bagging-hybrid | true | true | true | tf |
https://paperswithcode.com/paper/2003-13328 | Strip Pooling: Rethinking Spatial Pooling for Scene Parsing | 2003.13328 | https://arxiv.org/abs/2003.13328v1 | https://arxiv.org/pdf/2003.13328v1.pdf | https://github.com/Andrew-Qibin/SPNet | true | true | true | pytorch |
https://paperswithcode.com/paper/masked-face-recognition-dataset-and | Masked Face Recognition Dataset and Application | 2003.09093 | https://arxiv.org/abs/2003.09093v2 | https://arxiv.org/pdf/2003.09093v2.pdf | https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset | true | true | true | none |
https://paperswithcode.com/paper/adversarial-texture-optimization-from-rgb-d | Adversarial Texture Optimization from RGB-D Scans | 2003.08400 | https://arxiv.org/abs/2003.08400v1 | https://arxiv.org/pdf/2003.08400v1.pdf | https://github.com/hjwdzh/AdversarialTexture | true | true | false | tf |
https://paperswithcode.com/paper/covid-19-the-first-public-coronavirus-twitter | COVID-19: The First Public Coronavirus Twitter Dataset | 2003.07372 | https://arxiv.org/abs/2003.07372v1 | https://arxiv.org/pdf/2003.07372v1.pdf | https://github.com/echen102/COVID-19-TweetIDs | true | true | true | none |
https://paperswithcode.com/paper/semantically-enriched-search-engine-for | Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online | 2003.06561 | https://arxiv.org/abs/2003.06561v1 | https://arxiv.org/pdf/2003.06561v1.pdf | https://github.com/gengchenmai/arcgis-online-search-engine | true | true | false | none |
https://paperswithcode.com/paper/caesar-source-finder-recent-developments-and | CAESAR source finder: recent developments and testing | 1909.06116 | https://arxiv.org/abs/1909.06116v1 | https://arxiv.org/pdf/1909.06116v1.pdf | https://github.com/SKA-INAF/caesar | true | true | false | none |
https://paperswithcode.com/paper/select-and-attend-towards-controllable | Select and Attend: Towards Controllable Content Selection in Text Generation | 1909.04453 | https://arxiv.org/abs/1909.04453v1 | https://arxiv.org/pdf/1909.04453v1.pdf | https://github.com/chin-gyou/controllable-selection | true | true | false | pytorch |
https://paperswithcode.com/paper/3dsiamesenet-to-analyze-brain-mri | 3DSiameseNet to Analyze Brain MRI | 1909.01098 | https://arxiv.org/abs/1909.01098v1 | https://arxiv.org/pdf/1909.01098v1.pdf | https://github.com/morphoboid/3D-SiameseNet | true | true | false | tf |
https://paperswithcode.com/paper/relation-aware-entity-alignment-for | Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs | 1908.08210 | https://arxiv.org/abs/1908.08210v1 | https://arxiv.org/pdf/1908.08210v1.pdf | https://github.com/StephanieWyt/RDGCN | true | true | true | tf |
https://paperswithcode.com/paper/190807836 | PubLayNet: largest dataset ever for document layout analysis | 1908.07836 | https://arxiv.org/abs/1908.07836v1 | https://arxiv.org/pdf/1908.07836v1.pdf | https://github.com/ibm-aur-nlp/PubLayNet | true | true | true | none |
https://paperswithcode.com/paper/prosodic-phrase-alignment-for-machine-dubbing | Prosodic Phrase Alignment for Machine Dubbing | 1908.07226 | https://arxiv.org/abs/1908.07226v1 | https://arxiv.org/pdf/1908.07226v1.pdf | https://github.com/alpoktem/MachineDub | true | true | true | none |
https://paperswithcode.com/paper/videonavqa-bridging-the-gap-between-visual | VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering | 1908.04950 | https://arxiv.org/abs/1908.04950v1 | https://arxiv.org/pdf/1908.04950v1.pdf | https://github.com/catalina17/VideoNavQA | true | true | true | pytorch |
https://paperswithcode.com/paper/domain-specific-embedding-network-for-zero | Domain-Specific Embedding Network for Zero-Shot Recognition | 1908.04174 | https://arxiv.org/abs/1908.04174v1 | https://arxiv.org/pdf/1908.04174v1.pdf | https://github.com/mboboGO/DSEN-for-GZSL | true | true | false | pytorch |
https://paperswithcode.com/paper/consensus-maximization-tree-search-revisited | Consensus Maximization Tree Search Revisited | 1908.02021 | https://arxiv.org/abs/1908.02021v3 | https://arxiv.org/pdf/1908.02021v3.pdf | https://github.com/ZhipengCai/MaxConTreeSearch | true | true | true | none |
https://paperswithcode.com/paper/blebeacon-a-real-subject-trial-dataset-from | BLEBeacon: A Real-Subject Trial Dataset from Mobile Bluetooth Low Energy Beacons | 1802.08782 | https://arxiv.org/abs/1802.08782v2 | https://arxiv.org/pdf/1802.08782v2.pdf | https://github.com/dimisik/BLEBeacon-Dataset | true | true | true | none |
https://paperswithcode.com/paper/biological-and-shortest-path-routing | Biological and Shortest-Path Routing Procedures for Transportation Network Design | 1803.03528 | http://arxiv.org/abs/1803.03528v1 | http://arxiv.org/pdf/1803.03528v1.pdf | https://github.com/fqueyroi/tulip_plugins | true | true | true | none |
https://paperswithcode.com/paper/viable-dependency-parsing-as-sequence | Viable Dependency Parsing as Sequence Labeling | 1902.10505 | http://arxiv.org/abs/1902.10505v2 | http://arxiv.org/pdf/1902.10505v2.pdf | https://github.com/mstrise/dep2label | true | true | false | pytorch |
https://paperswithcode.com/paper/the-state-of-sparsity-in-deep-neural-networks | The State of Sparsity in Deep Neural Networks | 1902.09574 | http://arxiv.org/abs/1902.09574v1 | http://arxiv.org/pdf/1902.09574v1.pdf | https://github.com/ars-ashuha/variational-dropout-sparsifies-dnn | true | false | true | tf |
https://paperswithcode.com/paper/from-dark-matter-to-galaxies-with | From Dark Matter to Galaxies with Convolutional Networks | 1902.05965 | http://arxiv.org/abs/1902.05965v2 | http://arxiv.org/pdf/1902.05965v2.pdf | https://github.com/xz2139/From-Dark-Matter-to-Galaxies-with-Convolutional-Networks | true | true | false | pytorch |
https://paperswithcode.com/paper/forensic-similarity-for-digital-images | Forensic Similarity for Digital Images | 1902.04684 | https://arxiv.org/abs/1902.04684v2 | https://arxiv.org/pdf/1902.04684v2.pdf | https://gitlab.com/MISLgit/forensic-similarity-for-digital-images | true | true | false | tf |
https://paperswithcode.com/paper/out-of-sample-testing-for-gans | Out-of-Sample Testing for GANs | 1901.09557 | http://arxiv.org/abs/1901.09557v1 | http://arxiv.org/pdf/1901.09557v1.pdf | https://github.com/psanch21/EvalGAN | true | true | false | tf |
https://paperswithcode.com/paper/active-learning-with-gaussian-processes-for | Active Learning with Gaussian Processes for High Throughput Phenotyping | 1901.06803 | http://arxiv.org/abs/1901.06803v1 | http://arxiv.org/pdf/1901.06803v1.pdf | https://github.com/sumitsk/algp | true | true | true | pytorch |
https://paperswithcode.com/paper/domain-adaptation-for-semg-based-gesture | Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks | 1901.06958 | https://arxiv.org/abs/1901.06958v2 | https://arxiv.org/pdf/1901.06958v2.pdf | https://github.com/ketyi/2SRNN | true | true | true | tf |
https://paperswithcode.com/paper/metadata-embeddings-for-user-and-item-cold | Metadata Embeddings for User and Item Cold-start Recommendations | 1507.08439 | http://arxiv.org/abs/1507.08439v1 | http://arxiv.org/pdf/1507.08439v1.pdf | https://github.com/lyst/lightfm | true | true | false | none |
https://paperswithcode.com/paper/image-super-resolution-using-very-deep | Image Super-Resolution Using Very Deep Residual Channel Attention Networks | 1807.02758 | http://arxiv.org/abs/1807.02758v2 | http://arxiv.org/pdf/1807.02758v2.pdf | https://github.com/yulunzhang/RCAN | true | true | true | pytorch |
https://paperswithcode.com/paper/steganalysis-via-a-convolutional-neural | Steganalysis via a Convolutional Neural Network using Large Convolution Filters for Embedding Process with Same Stego Key | 1605.07946 | http://arxiv.org/abs/1605.07946v3 | http://arxiv.org/pdf/1605.07946v3.pdf | https://github.com/rcouturier/steganalysis_with_deep_learning | true | true | true | torch |
https://paperswithcode.com/paper/knowledge-matters-importance-of-prior | Knowledge Matters: Importance of Prior Information for Optimization | 1301.4083 | http://arxiv.org/abs/1301.4083v6 | http://arxiv.org/pdf/1301.4083v6.pdf | https://github.com/caglar/structured_mlp | true | true | false | none |
https://paperswithcode.com/paper/off-policy-general-value-functions-to | Off-Policy General Value Functions to Represent Dynamic Role Assignments in RoboCup 3D Soccer Simulation | 1402.4525 | http://arxiv.org/abs/1402.4525v1 | http://arxiv.org/pdf/1402.4525v1.pdf | https://github.com/samindaa/RLLib | true | true | false | none |
https://paperswithcode.com/paper/echoes-of-persuasion-the-effect-of-euphony-in | Echoes of Persuasion: The Effect of Euphony in Persuasive Communication | 1508.05817 | http://arxiv.org/abs/1508.05817v1 | http://arxiv.org/pdf/1508.05817v1.pdf | https://github.com/marcoguerini/paired_datasets_for_persuasion | true | false | false | none |
https://paperswithcode.com/paper/an-ensemble-method-to-produce-high-quality | An Ensemble Method to Produce High-Quality Word Embeddings (2016) | 1604.01692 | https://arxiv.org/abs/1604.01692v2 | https://arxiv.org/pdf/1604.01692v2.pdf | https://github.com/LuminosoInsight/conceptnet-vector-ensemble | true | true | false | none |
https://paperswithcode.com/paper/orientation-driven-bag-of-appearances-for | Orientation Driven Bag of Appearances for Person Re-identification | 1605.02464 | http://arxiv.org/abs/1605.02464v1 | http://arxiv.org/pdf/1605.02464v1.pdf | https://github.com/charliememory/PKU-Reid-Dataset | true | true | false | none |
https://paperswithcode.com/paper/wide-deep-learning-for-recommender-systems | Wide & Deep Learning for Recommender Systems | 1606.07792 | http://arxiv.org/abs/1606.07792v1 | http://arxiv.org/pdf/1606.07792v1.pdf | https://github.com/fengtong-xiao/DMBGN | false | false | true | pytorch |
https://paperswithcode.com/paper/intraoperative-margin-assessment-of-human | Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks | 1703.10827 | http://arxiv.org/abs/1703.10827v1 | http://arxiv.org/pdf/1703.10827v1.pdf | https://github.com/AmalRT/DNN_Reg | true | true | true | none |
https://paperswithcode.com/paper/stick-breaking-variational-autoencoders | Stick-Breaking Variational Autoencoders | 1605.06197 | http://arxiv.org/abs/1605.06197v3 | http://arxiv.org/pdf/1605.06197v3.pdf | https://github.com/enalisnick/stick-breaking_dgms | true | true | true | none |
https://paperswithcode.com/paper/msht-multi-stage-hybrid-transformer-for-the | MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer | 2112.13513 | https://arxiv.org/abs/2112.13513v1 | https://arxiv.org/pdf/2112.13513v1.pdf | https://github.com/sagizty/multi-stage-hybrid-transformer | true | true | false | pytorch |
https://paperswithcode.com/paper/expressive-explanations-of-dnns-by-combining | Expressive Explanations of DNNs by Combining Concept Analysis with ILP | 2105.07371 | https://arxiv.org/abs/2105.07371v1 | https://arxiv.org/pdf/2105.07371v1.pdf | https://github.com/mc-lovin-mlem/concept-embeddings-and-ilp | true | true | false | pytorch |
https://paperswithcode.com/paper/iteratively-trained-interactive-segmentation | Iteratively Trained Interactive Segmentation | 1805.04398 | http://arxiv.org/abs/1805.04398v1 | http://arxiv.org/pdf/1805.04398v1.pdf | https://github.com/sabarim/itis | true | false | false | tf |
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