Datasets:
pwc-archive
/
links-between-paper-and-code

Dataset card Data Studio Files
xet
Community
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bool
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https://paperswithcode.com/paper/autofis-automatic-feature-interaction
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
2003.11235
https://arxiv.org/abs/2003.11235v3
https://arxiv.org/pdf/2003.11235v3.pdf
https://github.com/renmada/PaddleRec/tree/autofis/models/rank/autofis
false
false
false
paddle
https://paperswithcode.com/paper/unsupervised-feature-selection-based-on
Unsupervised Feature Selection based on Adaptive Similarity Learning and Subspace Clustering
1912.05458
https://arxiv.org/abs/1912.05458v1
https://arxiv.org/pdf/1912.05458v1.pdf
https://github.com/mohsengh/SCFS
true
false
false
none
https://paperswithcode.com/paper/tracking-without-bells-and-whistles
Tracking without bells and whistles
1903.05625
https://arxiv.org/abs/1903.05625v3
https://arxiv.org/pdf/1903.05625v3.pdf
https://github.com/xiuyu0000/new_papers_codes/tree/main/rbpn
false
false
false
mindspore
https://paperswithcode.com/paper/deep-shape-matching
Deep Shape Matching
1709.03409
http://arxiv.org/abs/1709.03409v2
http://arxiv.org/pdf/1709.03409v2.pdf
https://github.com/janesjanes/sketchy
true
true
false
none
https://paperswithcode.com/paper/measuring-association-with-wasserstein
Measuring association with Wasserstein distances
2102.00356
https://arxiv.org/abs/2102.00356v3
https://arxiv.org/pdf/2102.00356v3.pdf
https://github.com/johanneswiesel/Wasserstein-correlation
true
true
false
none
https://paperswithcode.com/paper/aspect-controllable-opinion-summarization
Aspect-Controllable Opinion Summarization
2109.03171
https://arxiv.org/abs/2109.03171v1
https://arxiv.org/pdf/2109.03171v1.pdf
https://github.com/rktamplayo/acesum
true
true
false
pytorch
https://paperswithcode.com/paper/cbnetv2-a-composite-backbone-network
CBNet: A Composite Backbone Network Architecture for Object Detection
2107.00420
https://arxiv.org/abs/2107.00420v7
https://arxiv.org/pdf/2107.00420v7.pdf
https://github.com/shinya7y/UniverseNet
false
false
false
pytorch
https://paperswithcode.com/paper/high-resolution-waveform-capture-device-on-a
High-Resolution Waveform Capture Device on a Cyclone-V FPGA
2109.03026
https://arxiv.org/abs/2109.03026v1
https://arxiv.org/pdf/2109.03026v1.pdf
https://github.com/noeloikeau/fpyga
true
true
false
none
https://paperswithcode.com/paper/fdfb-full-domain-functional-bootstrapping
FDFB: Full Domain Functional Bootstrapping Towards Practical Fully Homomorphic Encryption
2109.02731
https://arxiv.org/abs/2109.02731v1
https://arxiv.org/pdf/2109.02731v1.pdf
https://github.com/cispa/full-domain-functional-bootstrap
true
true
false
none
https://paperswithcode.com/paper/trust-region-policy-optimization
Trust Region Policy Optimization
1502.05477
http://arxiv.org/abs/1502.05477v5
http://arxiv.org/pdf/1502.05477v5.pdf
https://github.com/DLR-RM/stable-baselines3
false
false
false
pytorch
https://paperswithcode.com/paper/ema-auditing-data-removal-from-trained-models
EMA: Auditing Data Removal from Trained Models
2109.03675
https://arxiv.org/abs/2109.03675v2
https://arxiv.org/pdf/2109.03675v2.pdf
https://github.com/hazelsuko07/ema
true
true
false
pytorch
https://paperswithcode.com/paper/learning-formation-of-physically-based-face
Learning Formation of Physically-Based Face Attributes
2004.03458
https://arxiv.org/abs/2004.03458v2
https://arxiv.org/pdf/2004.03458v2.pdf
https://github.com/ICT-VGL/ICT-FaceKit
true
false
false
none
https://paperswithcode.com/paper/image-to-image-translation-with-conditional
Image-to-Image Translation with Conditional Adversarial Networks
1611.07004
http://arxiv.org/abs/1611.07004v3
http://arxiv.org/pdf/1611.07004v3.pdf
https://github.com/hahahappyboy/GANForCartoon
false
false
true
pytorch
https://paperswithcode.com/paper/u-gat-it-unsupervised-generative-attentional
U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
1907.10830
https://arxiv.org/abs/1907.10830v4
https://arxiv.org/pdf/1907.10830v4.pdf
https://github.com/hahahappyboy/GANForCartoon
false
false
true
pytorch
https://paperswithcode.com/paper/u-2-net-going-deeper-with-nested-u-structure
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
2005.09007
https://arxiv.org/abs/2005.09007v3
https://arxiv.org/pdf/2005.09007v3.pdf
https://github.com/hahahappyboy/GANForCartoon
false
false
true
pytorch
https://paperswithcode.com/paper/icassp-2022-acoustic-echo-cancellation
ICASSP 2022 Acoustic Echo Cancellation Challenge
2202.13290
https://arxiv.org/abs/2202.13290v1
https://arxiv.org/pdf/2202.13290v1.pdf
https://github.com/microsoft/AEC-Challenge
true
true
false
none
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/hahahappyboy/GANForCartoon
false
false
true
pytorch
https://paperswithcode.com/paper/glioblastoma-multiforme-prognosis-mri-missing
Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival Prediction
2104.01149
https://arxiv.org/abs/2104.01149v2
https://arxiv.org/pdf/2104.01149v2.pdf
https://github.com/mobarakol/GBM_Prognosis
true
false
true
pytorch
https://paperswithcode.com/paper/an-investigation-of-ibm-quantum-computing
An investigation of IBM Quantum Computing device performance on Combinatorial Optimisation Problems
2107.03638
https://arxiv.org/abs/2107.03638v3
https://arxiv.org/pdf/2107.03638v3.pdf
https://github.com/QuCO-CSAM/Solving-Combinatorial-Optimisation-Problems-Using-Quantum-Algorithms
false
false
true
none
https://paperswithcode.com/paper/bag-of-tricks-for-image-classification-with
Bag of Tricks for Image Classification with Convolutional Neural Networks
1812.01187
http://arxiv.org/abs/1812.01187v2
http://arxiv.org/pdf/1812.01187v2.pdf
https://github.com/JIHOO97/Image-Classification-Competition
false
false
true
none
https://paperswithcode.com/paper/simam-a-simple-parameter-free-attention
SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
null
http://proceedings.mlr.press/v139/yang21o.html
http://proceedings.mlr.press/v139/yang21o/yang21o.pdf
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/SimAM.py
false
false
false
mindspore
https://paperswithcode.com/paper/non-autoregressive-translation-with-layer
Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision
2110.07515
https://arxiv.org/abs/2110.07515v1
https://arxiv.org/pdf/2110.07515v1.pdf
https://github.com/chenyangh/dslp
true
true
true
pytorch
https://paperswithcode.com/paper/recpipe-co-designing-models-and-hardware-to
RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance
2105.08820
https://arxiv.org/abs/2105.08820v2
https://arxiv.org/pdf/2105.08820v2.pdf
https://github.com/harvard-acc/RecPipe
false
false
true
pytorch
https://paperswithcode.com/paper/attention-u-net-learning-where-to-look-for
Attention U-Net: Learning Where to Look for the Pancreas
1804.03999
http://arxiv.org/abs/1804.03999v3
http://arxiv.org/pdf/1804.03999v3.pdf
https://github.com/MasoumehVahedi/Brain_MRI_Segmentation
false
false
true
tf
https://paperswithcode.com/paper/normal-assisted-stereo-depth-estimation
Normal Assisted Stereo Depth Estimation
1911.10444
https://arxiv.org/abs/1911.10444v3
https://arxiv.org/pdf/1911.10444v3.pdf
https://github.com/udaykusupati/Normal-Assisted-Stereo
true
false
false
pytorch
https://paperswithcode.com/paper/on-the-feasibility-of-modeling-ofdm
Feasibility of Modeling Orthogonal Frequency-Division Multiplexing Communication Signals with Unsupervised Generative Adversarial Networks
2109.05107
https://arxiv.org/abs/2109.05107v2
https://arxiv.org/pdf/2109.05107v2.pdf
https://github.com/usnistgov/ofdm-gan
true
true
false
pytorch
https://paperswithcode.com/paper/scalable-bottom-up-hierarchical-clustering
Scalable Hierarchical Agglomerative Clustering
2010.11821
https://arxiv.org/abs/2010.11821v3
https://arxiv.org/pdf/2010.11821v3.pdf
https://github.com/nmonath/graphgrove
false
false
false
none
https://paperswithcode.com/paper/gradient-imitation-reinforcement-learning-for
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
2109.06415
https://arxiv.org/abs/2109.06415v1
https://arxiv.org/pdf/2109.06415v1.pdf
https://github.com/thu-bpm/gradlre
true
true
true
pytorch
https://paperswithcode.com/paper/bigrams-and-bilstms-two-neural-networks-for
Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection
null
https://aclanthology.org/W18-0911
https://aclanthology.org/W18-0911.pdf
https://github.com/GU-CLASP/ocota
true
true
false
none
https://paperswithcode.com/paper/gate-free-state-preparation-for-fast
Gate-free state preparation for fast variational quantum eigensolver simulations: ctrl-VQE
2008.04302
https://arxiv.org/abs/2008.04302v3
https://arxiv.org/pdf/2008.04302v3.pdf
https://github.com/mayhallgroup/ctrlq
true
true
true
none
https://paperswithcode.com/paper/fast-r-cnn
Fast R-CNN
1504.08083
http://arxiv.org/abs/1504.08083v2
http://arxiv.org/pdf/1504.08083v2.pdf
https://github.com/sbetageri/MaskRCNN
false
false
true
pytorch
https://paperswithcode.com/paper/generating-fine-grained-open-vocabulary
Generating Fine-Grained Open Vocabulary Entity Type Descriptions
1805.10564
http://arxiv.org/abs/1805.10564v1
http://arxiv.org/pdf/1805.10564v1.pdf
https://github.com/kingsaint/Open-vocabulary-entity-type-description
true
true
false
pytorch
https://paperswithcode.com/paper/universal-style-transfer-via-feature
Universal Style Transfer via Feature Transforms
1705.08086
http://arxiv.org/abs/1705.08086v2
http://arxiv.org/pdf/1705.08086v2.pdf
https://github.com/YCJGG/Muti-style-transfer-by-camera
false
false
true
pytorch
https://paperswithcode.com/paper/revisiting-the-inverted-indices-for-billion
Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors
1802.02422
http://arxiv.org/abs/1802.02422v2
http://arxiv.org/pdf/1802.02422v2.pdf
https://github.com/xinyandai/pnsw
false
false
true
mxnet
https://paperswithcode.com/paper/automatic-design-of-mechanical-metamaterial
Automatic Design of Mechanical Metamaterial Actuators
2002.03032
https://arxiv.org/abs/2002.03032v1
https://arxiv.org/pdf/2002.03032v1.pdf
https://github.com/ComplexityBiosystems/metamech
false
false
true
none
https://paperswithcode.com/paper/matrix-completion-in-the-unit-hypercube-via
Matrix Completion in the Unit Hypercube via Structured Matrix Factorization
1905.12881
https://arxiv.org/abs/1905.12881v1
https://arxiv.org/pdf/1905.12881v1.pdf
https://github.com/e-bug/unit-mf
true
true
true
none
https://paperswithcode.com/paper/neural-machine-translation-by-jointly
Neural Machine Translation by Jointly Learning to Align and Translate
1409.0473
http://arxiv.org/abs/1409.0473v7
http://arxiv.org/pdf/1409.0473v7.pdf
https://github.com/Epoch-Mengying/Generating-Poetry-with-Chatbot
false
false
true
none
https://paperswithcode.com/paper/soft-actor-critic-off-policy-maximum-entropy
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
1801.01290
http://arxiv.org/abs/1801.01290v2
http://arxiv.org/pdf/1801.01290v2.pdf
https://github.com/yimingpeng/sac-master
false
false
true
tf
https://paperswithcode.com/paper/constrained-bayesian-optimization-for
Constrained Bayesian Optimization for Automatic Chemical Design
1709.05501
https://arxiv.org/abs/1709.05501v6
https://arxiv.org/pdf/1709.05501v6.pdf
https://github.com/Ryan-Rhys/Constrained-Bayesian-Optimisation-for-Automatic-Chemical-Design
true
true
true
none
https://paperswithcode.com/paper/a-topological-centrality-measure-for-directed
A Topological Centrality Measure for Directed Networks
2201.12907
https://arxiv.org/abs/2201.12907v1
https://arxiv.org/pdf/2201.12907v1.pdf
https://github.com/lindahe8989/a-topological-centrality-measure-for-directed-networks
true
true
false
none
https://paperswithcode.com/paper/learning-to-execute
Learning to Execute
1410.4615
http://arxiv.org/abs/1410.4615v3
http://arxiv.org/pdf/1410.4615v3.pdf
https://github.com/btc-room101/bitcoin-rnn
false
false
true
none
https://paperswithcode.com/paper/mer-2023-multi-label-learning-modality
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
2304.08981
https://arxiv.org/abs/2304.08981v2
https://arxiv.org/pdf/2304.08981v2.pdf
https://github.com/zeroqiaoba/explainable-multimodal-emotion-reasoning
false
false
true
pytorch
https://paperswithcode.com/paper/trust-region-policy-optimization
Trust Region Policy Optimization
1502.05477
http://arxiv.org/abs/1502.05477v5
http://arxiv.org/pdf/1502.05477v5.pdf
https://github.com/sirdr/TRPO.jl
false
false
true
none
https://paperswithcode.com/paper/fast-end-to-end-learning-on-protein-surfaces
Fast End-to-End Learning on Protein Surfaces
null
http://openaccess.thecvf.com//content/CVPR2021/html/Sverrisson_Fast_End-to-End_Learning_on_Protein_Surfaces_CVPR_2021_paper.html
http://openaccess.thecvf.com//content/CVPR2021/papers/Sverrisson_Fast_End-to-End_Learning_on_Protein_Surfaces_CVPR_2021_paper.pdf
https://github.com/FreyrS/dMaSIF
true
false
false
pytorch
https://paperswithcode.com/paper/optimizing-frameworks-performance-using-c
Optimizing Frameworks Performance Using C++ Modules Aware ROOT
1812.03992
http://arxiv.org/abs/1812.03992v1
http://arxiv.org/pdf/1812.03992v1.pdf
https://github.com/yamaguchi1024/CHEP2018-Cplusplus-Modules
false
false
true
none
https://paperswithcode.com/paper/closed-form-analytical-results-for-maximum
Entropy Regularized Reinforcement Learning Using Large Deviation Theory
2106.03931
https://arxiv.org/abs/2106.03931v2
https://arxiv.org/pdf/2106.03931v2.pdf
https://github.com/argearriojas/2023-entregrl
true
true
false
none
https://paperswithcode.com/paper/quantum-circuit-learning
Quantum Circuit Learning
1803.00745
http://arxiv.org/abs/1803.00745v1
http://arxiv.org/pdf/1803.00745v1.pdf
https://github.com/UnofficialJuliaMirrorSnapshots/Yao.jl-5872b779-8223-5990-8dd0-5abbb0748c8c
false
false
true
none
https://paperswithcode.com/paper/feed-forward-networks-with-attention-can
Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems
1512.08756
http://arxiv.org/abs/1512.08756v5
http://arxiv.org/pdf/1512.08756v5.pdf
https://github.com/WenYanger/Contextual-Attention
false
false
true
pytorch
https://paperswithcode.com/paper/predicting-molecular-dipole-moments-by
Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles
2003.12437
https://arxiv.org/abs/2003.12437v3
https://arxiv.org/pdf/2003.12437v3.pdf
https://github.com/max-veit/velociraptor
true
true
true
none
https://paperswithcode.com/paper/superforms-tropical-cohomology-and-poincare
Superforms, Tropical Cohomology, and Poincaré Duality
1512.07409
http://arxiv.org/abs/1512.07409v3
http://arxiv.org/pdf/1512.07409v3.pdf
https://github.com/lkastner/cellularSheaves
false
false
true
none
https://paperswithcode.com/paper/chinese-poetry-generation-with-planning-based
Chinese Poetry Generation with Planning based Neural Network
1610.09889
http://arxiv.org/abs/1610.09889v2
http://arxiv.org/pdf/1610.09889v2.pdf
https://github.com/Epoch-Mengying/Generating-Poetry-with-Chatbot
false
false
true
none
https://paperswithcode.com/paper/monitoring-data-requests-in-decentralized
Monitoring Data Requests in Decentralized Data Storage Systems: A Case Study of IPFS
2104.09202
https://arxiv.org/abs/2104.09202v5
https://arxiv.org/pdf/2104.09202v5.pdf
https://github.com/mrd0ll4r/ipfs-tools
true
true
true
none
https://paperswithcode.com/paper/feature-selection-in-omics-prediction
Feature selection in omics prediction problems using cat scores and false nondiscovery rate control
0903.2003
http://arxiv.org/abs/0903.2003v4
http://arxiv.org/pdf/0903.2003v4.pdf
https://github.com/mjafin/shrinkage_da
false
false
true
none
https://paperswithcode.com/paper/value-prediction-network
Value Prediction Network
1707.03497
http://arxiv.org/abs/1707.03497v2
http://arxiv.org/pdf/1707.03497v2.pdf
https://github.com/junhyukoh/value-prediction-network
true
true
true
tf
https://paperswithcode.com/paper/graph-neural-networks-for-icecube-signal
Graph Neural Networks for IceCube Signal Classification
1809.06166
http://arxiv.org/abs/1809.06166v1
http://arxiv.org/pdf/1809.06166v1.pdf
https://github.com/WIPACrepo/NuIntClassification
false
false
true
pytorch
https://paperswithcode.com/paper/robust-inference-via-generative-classifiers
Robust Inference via Generative Classifiers for Handling Noisy Labels
1901.11300
https://arxiv.org/abs/1901.11300v2
https://arxiv.org/pdf/1901.11300v2.pdf
https://github.com/pokaxpoka/RoGNoisyLabel
true
true
true
pytorch
https://paperswithcode.com/paper/deep-learning-of-vortex-induced-vibrations
Deep Learning of Vortex Induced Vibrations
1808.08952
http://arxiv.org/abs/1808.08952v1
http://arxiv.org/pdf/1808.08952v1.pdf
https://github.com/maziarraissi/DeepVIV
true
true
true
tf
https://paperswithcode.com/paper/190503448
parasweep: A template-based utility for generating, dispatching, and post-processing of parameter sweeps
1905.03448
https://arxiv.org/abs/1905.03448v2
https://arxiv.org/pdf/1905.03448v2.pdf
https://github.com/eviatarbach/parasweep
true
true
true
none
https://paperswithcode.com/paper/analysis-of-the-impact-of-negative-sampling
Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs
1708.06816
http://arxiv.org/abs/1708.06816v2
http://arxiv.org/pdf/1708.06816v2.pdf
https://github.com/bhushank/kge-rl
true
true
true
pytorch
https://paperswithcode.com/paper/190600411
TechNet: Technology Semantic Network Based on Patent Data
1906.00411
https://arxiv.org/abs/1906.00411v4
https://arxiv.org/pdf/1906.00411v4.pdf
https://github.com/SerhadS/TechNet
true
true
true
none
https://paperswithcode.com/paper/deeppicar-a-low-cost-deep-neural-network
DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car
1712.08644
https://arxiv.org/abs/1712.08644v4
https://arxiv.org/pdf/1712.08644v4.pdf
https://github.com/heechul/picar
true
true
true
tf
https://paperswithcode.com/paper/regularizing-activation-distribution-for
Regularizing Activation Distribution for Training Binarized Deep Networks
1904.02823
http://arxiv.org/abs/1904.02823v1
http://arxiv.org/pdf/1904.02823v1.pdf
https://github.com/ruizhoud/DistributionLoss
true
true
true
pytorch
https://paperswithcode.com/paper/a-simple-baseline-for-multi-object-tracking
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
2004.01888
https://arxiv.org/abs/2004.01888v6
https://arxiv.org/pdf/2004.01888v6.pdf
https://github.com/nadinenijssen/Github_5AUA0_Project_G12T1
false
false
true
pytorch
https://paperswithcode.com/paper/mediation-effects-that-emulate-a-target
Mediation effects that emulate a target randomised trial: Simulation-based evaluation of ill-defined interventions on multiple mediators
1907.06734
https://arxiv.org/abs/1907.06734v3
https://arxiv.org/pdf/1907.06734v3.pdf
https://github.com/moreno-betancur/medRCT
true
true
true
none
https://paperswithcode.com/paper/towards-better-validity-dispersion-based
Towards better Validity: Dispersion based Clustering for Unsupervised Person Re-identification
1906.01308
https://arxiv.org/abs/1906.01308v1
https://arxiv.org/pdf/1906.01308v1.pdf
https://github.com/gddingcs/Dispersion-based-Clustering
true
true
true
pytorch
https://paperswithcode.com/paper/optimal-hyperparameters-for-deep-lstm
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
1707.06799
http://arxiv.org/abs/1707.06799v2
http://arxiv.org/pdf/1707.06799v2.pdf
https://github.com/SuphanutN/Thai-NER-BiLSTMCRF-WordCharEmbedding
false
false
true
none
https://paperswithcode.com/paper/convolutional-image-captioning
Convolutional Image Captioning
1711.09151
http://arxiv.org/abs/1711.09151v1
http://arxiv.org/pdf/1711.09151v1.pdf
https://github.com/davinhill/Convolution_Captioning
false
false
true
pytorch
https://paperswithcode.com/paper/171110609
A recurrent neural network for classification of unevenly sampled variable stars
1711.10609
http://arxiv.org/abs/1711.10609v1
http://arxiv.org/pdf/1711.10609v1.pdf
https://github.com/bthtsang/DeepClassifierNoveltyDetection
false
false
true
tf
https://paperswithcode.com/paper/openloris-object-a-dataset-and-benchmark
OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning
1911.06487
https://arxiv.org/abs/1911.06487v2
https://arxiv.org/pdf/1911.06487v2.pdf
https://github.com/sheqi/Continual_Learning_CV
false
false
true
pytorch
https://paperswithcode.com/paper/towards-better-understanding-of-gradient
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
1711.06104
http://arxiv.org/abs/1711.06104v4
http://arxiv.org/pdf/1711.06104v4.pdf
https://github.com/pytorch/captum
false
false
false
pytorch
https://paperswithcode.com/paper/why-should-i-trust-you-explaining-the
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
1602.04938
http://arxiv.org/abs/1602.04938v3
http://arxiv.org/pdf/1602.04938v3.pdf
https://github.com/pytorch/captum
false
false
false
pytorch
https://paperswithcode.com/paper/local-explanation-methods-for-deep-neural
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values
1810.03307
http://arxiv.org/abs/1810.03307v1
http://arxiv.org/pdf/1810.03307v1.pdf
https://github.com/pytorch/captum
false
false
false
pytorch
https://paperswithcode.com/paper/speech-denoising-with-deep-feature-losses
Speech Denoising with Deep Feature Losses
1806.10522
http://arxiv.org/abs/1806.10522v1
http://arxiv.org/pdf/1806.10522v1.pdf
https://github.com/kuntojirohan/speechdenoising
false
false
true
tf
https://paperswithcode.com/paper/viwi-vision-aided-mmwave-beam-tracking
ViWi Vision-Aided mmWave Beam Tracking: Dataset, Task, and Baseline Solutions
2002.02445
https://arxiv.org/abs/2002.02445v3
https://arxiv.org/pdf/2002.02445v3.pdf
https://github.com/malrabeiah/VABT
true
true
true
pytorch
https://paperswithcode.com/paper/classification-with-costly-features-as-a
Classification with Costly Features as a Sequential Decision-Making Problem
1909.02564
https://arxiv.org/abs/1909.02564v1
https://arxiv.org/pdf/1909.02564v1.pdf
https://github.com/jaromiru/cwcf
true
true
true
pytorch
https://paperswithcode.com/paper/derandomized-smoothing-for-certifiable
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks
2002.10733
https://arxiv.org/abs/2002.10733v3
https://arxiv.org/pdf/2002.10733v3.pdf
https://github.com/alevine0/patchSmoothing
true
true
true
pytorch
https://paperswithcode.com/paper/facenet-a-unified-embedding-for-face
FaceNet: A Unified Embedding for Face Recognition and Clustering
1503.03832
http://arxiv.org/abs/1503.03832v3
http://arxiv.org/pdf/1503.03832v3.pdf
https://github.com/zexUlt/facerec
false
false
true
mxnet
https://paperswithcode.com/paper/a-generative-model-of-software-dependency
A Generative Model of Software Dependency Graphs to Better Understand Software Evolution
1410.7921
http://arxiv.org/abs/1410.7921v3
http://arxiv.org/pdf/1410.7921v3.pdf
https://github.com/v-m/GDGNC
true
true
true
none
https://paperswithcode.com/paper/acceleration-of-large-margin-metric-learning
Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling
2009.14244
https://arxiv.org/abs/2009.14244v1
https://arxiv.org/pdf/2009.14244v1.pdf
https://github.com/bghojogh/Large-Margin-Metric-Learning
true
false
true
none
https://paperswithcode.com/paper/bullseye-polytope-a-scalable-clean-label
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability
2005.00191
https://arxiv.org/abs/2005.00191v3
https://arxiv.org/pdf/2005.00191v3.pdf
https://github.com/ucsb-seclab/BullseyePoison
true
true
true
pytorch
https://paperswithcode.com/paper/photo-realistic-single-image-super-resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
1609.04802
http://arxiv.org/abs/1609.04802v5
http://arxiv.org/pdf/1609.04802v5.pdf
https://github.com/MindSpore-paper-code-2/code3/tree/main/wdsr
false
false
false
mindspore
https://paperswithcode.com/paper/towards-exploiting-sticker-for-multimodal
Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline
null
https://aclanthology.org/2022.coling-1.591
https://aclanthology.org/2022.coling-1.591.pdf
https://github.com/logos23333/csmsa
true
true
false
none
https://paperswithcode.com/paper/deep-learning-algorithms-for-rotating
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study
2003.03315
https://arxiv.org/abs/2003.03315v3
https://arxiv.org/pdf/2003.03315v3.pdf
https://github.com/ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark
true
true
true
pytorch
https://paperswithcode.com/paper/inference-in-the-stochastic-block-model-with
Reliable Time Prediction in the Markov Stochastic Block Model
2004.04402
https://arxiv.org/abs/2004.04402v3
https://arxiv.org/pdf/2004.04402v3.pdf
https://github.com/quentin-duchemin/inference-markovian-SBM
true
true
true
none
https://paperswithcode.com/paper/repbert-contextualized-text-embeddings-for
RepBERT: Contextualized Text Embeddings for First-Stage Retrieval
2006.15498
https://arxiv.org/abs/2006.15498v2
https://arxiv.org/pdf/2006.15498v2.pdf
https://github.com/jingtaozhan/RepBERT-Index
true
true
true
pytorch
https://paperswithcode.com/paper/self-supervised-character-to-character
Self-supervised Character-to-Character Distillation for Text Recognition
2211.00288
https://arxiv.org/abs/2211.00288v4
https://arxiv.org/pdf/2211.00288v4.pdf
https://github.com/tongkunguan/ccd
true
true
true
pytorch
https://paperswithcode.com/paper/transformer-hawkes-process
Transformer Hawkes Process
2002.09291
https://arxiv.org/abs/2002.09291v5
https://arxiv.org/pdf/2002.09291v5.pdf
https://github.com/SimiaoZuo/Transformer-Hawkes-Process
true
true
true
pytorch
https://paperswithcode.com/paper/dicoderma-a-practical-approach-for-metadata
DICODerma: A practical approach for metadata management of images in dermatology
2102.08673
https://arxiv.org/abs/2102.08673v1
https://arxiv.org/pdf/2102.08673v1.pdf
https://github.com/dermatologist/dicom-dermatology
true
true
true
none
https://paperswithcode.com/paper/a-linguistic-analysis-of-visually-grounded
A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions
2010.03127
https://arxiv.org/abs/2010.03127v1
https://arxiv.org/pdf/2010.03127v1.pdf
https://github.com/Alab-NII/onecommon
true
true
true
tf
https://paperswithcode.com/paper/how-collective-asperity-detachments-nucleate
How collective asperity detachments nucleate slip at frictional interfaces
1904.07635
https://arxiv.org/abs/1904.07635v1
https://arxiv.org/pdf/1904.07635v1.pdf
https://github.com/tdegeus/GMatElastoPlasticQPot
false
false
true
none
https://paperswithcode.com/paper/non-negative-networks-against-adversarial
Non-Negative Networks Against Adversarial Attacks
1806.06108
http://arxiv.org/abs/1806.06108v2
http://arxiv.org/pdf/1806.06108v2.pdf
https://github.com/endgameinc/malware_evasion_competition
false
false
true
pytorch
https://paperswithcode.com/paper/primal-dual-algorithms-for-non-negative
Primal-Dual Algorithms for Non-negative Matrix Factorization with the Kullback-Leibler Divergence
1412.1788
http://arxiv.org/abs/1412.1788v1
http://arxiv.org/pdf/1412.1788v1.pdf
https://github.com/felipeyanez/nmf
false
false
true
none
https://paperswithcode.com/paper/malware-detection-by-eating-a-whole-exe
Malware Detection by Eating a Whole EXE
1710.09435
http://arxiv.org/abs/1710.09435v1
http://arxiv.org/pdf/1710.09435v1.pdf
https://github.com/endgameinc/malware_evasion_competition
false
false
true
pytorch
https://paperswithcode.com/paper/explaining-how-deep-neural-networks-forget-by
Explaining How Deep Neural Networks Forget by Deep Visualization
2005.01004
https://arxiv.org/abs/2005.01004v3
https://arxiv.org/pdf/2005.01004v3.pdf
https://github.com/giangnguyen2412/dissect_catastrophic_forgetting
true
false
true
pytorch
https://paperswithcode.com/paper/kwant-a-software-package-for-quantum
Kwant: a software package for quantum transport
1309.2926
https://arxiv.org/abs/1309.2926v2
https://arxiv.org/pdf/1309.2926v2.pdf
https://github.com/joel-hutchinson/Haldane-Bilayer-KWANT
false
false
true
none
https://paperswithcode.com/paper/aim-taking-answers-in-mind-to-correct-chinese
AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications
2208.12505
https://arxiv.org/abs/2208.12505v2
https://arxiv.org/pdf/2208.12505v2.pdf
https://github.com/yusenzhang826/aim
true
true
false
pytorch
https://paperswithcode.com/paper/joint-analysis-of-the-thermal-sunyaev
Joint analysis of the thermal Sunyaev-Zeldovich effect and 2MASS galaxies: Probing gas physics in the local Universe and beyond
1804.05008
https://arxiv.org/abs/1804.05008v3
https://arxiv.org/pdf/1804.05008v3.pdf
https://github.com/ryumakiya/pysz
false
false
true
none
https://paperswithcode.com/paper/14101465
The invariant extended Kalman filter as a stable observer
1410.1465
http://arxiv.org/abs/1410.1465v4
http://arxiv.org/pdf/1410.1465v4.pdf
https://github.com/artivis/kalmanif
false
false
true
none
https://paperswithcode.com/paper/clustering-longitudinal-life-course-sequences
Clustering Longitudinal Life-Course Sequences Using Mixtures of Exponential-Distance Models
1908.07963
https://arxiv.org/abs/1908.07963v4
https://arxiv.org/pdf/1908.07963v4.pdf
https://github.com/Keefe-Murphy/MEDseq
true
false
true
none
https://paperswithcode.com/paper/must-cnn-a-multilayer-shift-and-stitch-deep
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction
1605.03004
http://arxiv.org/abs/1605.03004v1
http://arxiv.org/pdf/1605.03004v1.pdf
https://github.com/QData/DeepProtein
false
false
true
torch