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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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