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

Dataset card Data Studio Files
xet
Community
1
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/an-exploration-of-embodied-visual-exploration
An Exploration of Embodied Visual Exploration
2001.02192
https://arxiv.org/abs/2001.02192v2
https://arxiv.org/pdf/2001.02192v2.pdf
https://github.com/facebookresearch/exploring_exploration
true
true
false
pytorch
https://paperswithcode.com/paper/understanding-the-disharmony-between-weight
Understanding the Disharmony between Weight Normalization Family and Weight Decay: $ε-$shifted $L_2$ Regularizer
1911.05920
https://arxiv.org/abs/1911.05920v1
https://arxiv.org/pdf/1911.05920v1.pdf
https://github.com/implus/PytorchInsight
true
true
false
pytorch
https://paperswithcode.com/paper/learning-deconvolution-network-for-semantic
Learning Deconvolution Network for Semantic Segmentation
1505.04366
http://arxiv.org/abs/1505.04366v1
http://arxiv.org/pdf/1505.04366v1.pdf
https://github.com/GoNgXiAoPeNg1/caffeBVLCplus
false
false
true
none
https://paperswithcode.com/paper/parameter-constrained-transfer-learning-for
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising
1910.06749
https://arxiv.org/abs/1910.06749v3
https://arxiv.org/pdf/1910.06749v3.pdf
https://github.com/90n9-yu/PT-WGAN
true
true
false
tf
https://paperswithcode.com/paper/deep-multi-view-learning-via-task-optimal-cca
Deep Multi-View Learning via Task-Optimal CCA
1907.07739
https://arxiv.org/abs/1907.07739v1
https://arxiv.org/pdf/1907.07739v1.pdf
https://github.com/hdcouture/TOCCA
false
false
true
tf
https://paperswithcode.com/paper/tar-generalized-forensic-framework-to-detect
TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised Learning
2105.06117
https://arxiv.org/abs/2105.06117v1
https://arxiv.org/pdf/2105.06117v1.pdf
https://github.com/Clench/TAR_resAE
true
true
false
none
https://paperswithcode.com/paper/a-divide-and-conquer-algorithm-for-quantum
A divide-and-conquer algorithm for quantum state preparation
2008.01511
https://arxiv.org/abs/2008.01511v2
https://arxiv.org/pdf/2008.01511v2.pdf
https://github.com/adjs/dcsp
false
false
true
none
https://paperswithcode.com/paper/learning-to-navigate-image-manifolds-induced
Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation
1901.11384
http://arxiv.org/abs/1901.11384v1
http://arxiv.org/pdf/1901.11384v1.pdf
https://github.com/belaalb/frameGAN
true
true
true
pytorch
https://paperswithcode.com/paper/mine-mutual-information-neural-estimation
MINE: Mutual Information Neural Estimation
1801.04062
https://arxiv.org/abs/1801.04062v5
https://arxiv.org/pdf/1801.04062v5.pdf
https://github.com/mboudiaf/Mutual-Information-Variational-Bounds
false
false
true
tf
https://paperswithcode.com/paper/representation-learning-with-contrastive
Representation Learning with Contrastive Predictive Coding
1807.03748
http://arxiv.org/abs/1807.03748v2
http://arxiv.org/pdf/1807.03748v2.pdf
https://github.com/mboudiaf/Mutual-Information-Variational-Bounds
false
false
true
tf
https://paperswithcode.com/paper/a-convnet-for-the-2020s
A ConvNet for the 2020s
2201.03545
https://arxiv.org/abs/2201.03545v2
https://arxiv.org/pdf/2201.03545v2.pdf
https://github.com/keras-team/keras/blob/master/keras/applications/convnext.py
false
false
false
tf
https://paperswithcode.com/paper/vilbert-pretraining-task-agnostic
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
1908.02265
https://arxiv.org/abs/1908.02265v1
https://arxiv.org/pdf/1908.02265v1.pdf
https://github.com/jialinwu17/tmpimgs
false
false
true
pytorch
https://paperswithcode.com/paper/an-ensemble-based-approach-to-click-through
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy
1711.01377
http://arxiv.org/abs/1711.01377v2
http://arxiv.org/pdf/1711.01377v2.pdf
https://github.com/cpapadimitriou/Click-Through-Rate-prediction
false
false
true
none
https://paperswithcode.com/paper/squeezedet-unified-small-low-power-fully
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
1612.01051
https://arxiv.org/abs/1612.01051v4
https://arxiv.org/pdf/1612.01051v4.pdf
https://github.com/vahidkhosh/squeezedet-keras
false
false
true
none
https://paperswithcode.com/paper/deep-generative-networks-for-sequence
Deep Generative Networks For Sequence Prediction
1804.06546
http://arxiv.org/abs/1804.06546v1
http://arxiv.org/pdf/1804.06546v1.pdf
https://github.com/mbeissinger/recurrent_gsn
true
true
false
none
https://paperswithcode.com/paper/android-malware-family-classification-based
Android Malware Family Classification Based on Resource Consumption over Time
1709.00875
https://arxiv.org/abs/1709.00875v1
https://arxiv.org/pdf/1709.00875v1.pdf
https://github.com/lucamassarelli/AMFC-BRCT
true
true
false
none
https://paperswithcode.com/paper/grid-tagging-scheme-for-aspect-oriented-fine
Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction
2010.04640
https://arxiv.org/abs/2010.04640v2
https://arxiv.org/pdf/2010.04640v2.pdf
https://github.com/l294265421/GTS-ASOTE
false
false
true
pytorch
https://paperswithcode.com/paper/fcos-fully-convolutional-one-stage-object
FCOS: Fully Convolutional One-Stage Object Detection
1904.01355
https://arxiv.org/abs/1904.01355v5
https://arxiv.org/pdf/1904.01355v5.pdf
https://github.com/vov-net/VoVNet-FCOS
false
false
true
pytorch
https://paperswithcode.com/paper/retinal-vessel-segmentation-based-on-fully-1
Retinal Vessel Segmentation based on Fully Convolutional Networks
1911.09915
https://arxiv.org/abs/1911.09915v1
https://arxiv.org/pdf/1911.09915v1.pdf
https://github.com/americofmoliveira/VesselSegmentation_ESWA
true
true
false
none
https://paperswithcode.com/paper/sentences-with-gapping-parsing-and
Sentences with Gapping: Parsing and Reconstructing Elided Predicates
1804.06922
http://arxiv.org/abs/1804.06922v1
http://arxiv.org/pdf/1804.06922v1.pdf
https://github.com/dialogue-evaluation/AGRR-2019
false
false
true
none
https://paperswithcode.com/paper/an-empirical-comparison-between-stochastic
An empirical comparison between stochastic and deterministic centroid initialisation for K-Means variations
1908.09946
https://arxiv.org/abs/1908.09946v6
https://arxiv.org/pdf/1908.09946v6.pdf
https://github.com/avouros/clustering-workplace
false
false
true
none
https://paperswithcode.com/paper/joint-semantic-mining-for-weakly-supervised
Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection
null
http://proceedings.neurips.cc/paper/2021/hash/642e92efb79421734881b53e1e1b18b6-Abstract.html
http://proceedings.neurips.cc/paper/2021/file/642e92efb79421734881b53e1e1b18b6-Paper.pdf
https://github.com/jiwei0921/jsm
true
true
false
pytorch
https://paperswithcode.com/paper/fair-dimensionality-reduction-and-iterative
Multi-Criteria Dimensionality Reduction with Applications to Fairness
1902.11281
https://arxiv.org/abs/1902.11281v3
https://arxiv.org/pdf/1902.11281v3.pdf
https://github.com/SDPforAll/multiCriteriaDimReduction
true
true
true
none
https://paperswithcode.com/paper/190109195
Variational approach to rare event simulation using least-squares regression
1901.09195
http://arxiv.org/abs/1901.09195v2
http://arxiv.org/pdf/1901.09195v2.pdf
https://github.com/lorenzrichter/BSDE
true
true
true
none
https://paperswithcode.com/paper/smart-contract-vulnerabilities-does-anyone
Smart Contract Vulnerabilities: Does Anyone Care?
1902.06710
http://arxiv.org/abs/1902.06710v2
http://arxiv.org/pdf/1902.06710v2.pdf
https://github.com/danhper/evm-analyzer
true
false
true
none
https://paperswithcode.com/paper/proximal-mean-field-for-neural-network
Proximal Mean-field for Neural Network Quantization
1812.04353
https://arxiv.org/abs/1812.04353v3
https://arxiv.org/pdf/1812.04353v3.pdf
https://github.com/tajanthan/pmf
true
true
true
pytorch
https://paperswithcode.com/paper/development-and-application-of-a
Development and Application of a Decentralized Domain Name Service
2412.01959
https://arxiv.org/abs/2412.01959v2
https://arxiv.org/pdf/2412.01959v2.pdf
https://github.com/GY19A/ddns
true
false
false
none
https://paperswithcode.com/paper/joint-ranking-svm-and-binary-relevance-with
Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification
1911.01658
https://arxiv.org/abs/1911.01658v1
https://arxiv.org/pdf/1911.01658v1.pdf
https://github.com/GuoqiangWoodrowWu/RBRL
true
true
false
none
https://paperswithcode.com/paper/scale-wise-convolution-for-image-restoration
Scale-wise Convolution for Image Restoration
1912.09028
https://arxiv.org/abs/1912.09028v1
https://arxiv.org/pdf/1912.09028v1.pdf
https://github.com/ychfan/scn
true
true
true
pytorch
https://paperswithcode.com/paper/deep-convolutional-neural-networks-for-12
Deep Convolutional Neural Networks for Thermal Infrared Object Tracking
null
https://www.sciencedirect.com/science/article/abs/pii/S0950705117303544
https://www.researchgate.net/publication/318714772_Deep_Convolutional_Neural_Networks_for_Thermal_Infrared_Object_Tracking
https://github.com/QiaoLiuHit/MCFTS
false
false
false
none
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/flora-zyx/SJNet
false
false
true
pytorch
https://paperswithcode.com/paper/measuring-spatial-allocative-efficiency-in
Measuring Spatial Allocative Efficiency in Basketball
1912.05129
https://arxiv.org/abs/1912.05129v2
https://arxiv.org/pdf/1912.05129v2.pdf
https://github.com/nsandholtz/lpl
true
true
true
none
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/karuj/StyleTransfer
false
false
true
pytorch
https://paperswithcode.com/paper/generalized-latency-performance-estimation
Generalized Latency Performance Estimation for Once-For-All Neural Architecture Search
2101.00732
https://arxiv.org/abs/2101.00732v1
https://arxiv.org/pdf/2101.00732v1.pdf
https://github.com/RhythmSyed/NAS_PerformanceEstimation
false
false
false
pytorch
https://paperswithcode.com/paper/physically-disentangled-intra-and-inter
Physically Disentangled Intra- and Inter-Domain Adaptation for Varicolored Haze Removal
null
http://openaccess.thecvf.com//content/CVPR2022/html/Li_Physically_Disentangled_Intra-_and_Inter-Domain_Adaptation_for_Varicolored_Haze_Removal_CVPR_2022_paper.html
http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Physically_Disentangled_Intra-_and_Inter-Domain_Adaptation_for_Varicolored_Haze_Removal_CVPR_2022_paper.pdf
https://github.com/huayuuu/pdi2a-cvpr2022
true
true
false
none
https://paperswithcode.com/paper/boosted-cascaded-convnets-for-multilabel
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
1711.08760
http://arxiv.org/abs/1711.08760v1
http://arxiv.org/pdf/1711.08760v1.pdf
https://github.com/Azure/AzureChestXRay
false
false
true
pytorch
https://paperswithcode.com/paper/bayesian-segnet-model-uncertainty-in-deep
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
1511.02680
http://arxiv.org/abs/1511.02680v2
http://arxiv.org/pdf/1511.02680v2.pdf
https://github.com/SkyWa7ch3r/ImageSegmentation
false
false
true
tf
https://paperswithcode.com/paper/encoder-decoder-with-atrous-separable
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
1802.02611
http://arxiv.org/abs/1802.02611v3
http://arxiv.org/pdf/1802.02611v3.pdf
https://github.com/SkyWa7ch3r/ImageSegmentation
false
false
true
tf
https://paperswithcode.com/paper/fast-scnn-fast-semantic-segmentation-network
Fast-SCNN: Fast Semantic Segmentation Network
1902.04502
http://arxiv.org/abs/1902.04502v1
http://arxiv.org/pdf/1902.04502v1.pdf
https://github.com/SkyWa7ch3r/ImageSegmentation
false
false
true
tf
https://paperswithcode.com/paper/drop-an-octave-reducing-spatial-redundancy-in
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
1904.05049
https://arxiv.org/abs/1904.05049v3
https://arxiv.org/pdf/1904.05049v3.pdf
https://github.com/tuanzhangCS/octconv_resnet
false
false
true
tf
https://paperswithcode.com/paper/semi-supervised-sequence-learning
Semi-supervised Sequence Learning
1511.01432
http://arxiv.org/abs/1511.01432v1
http://arxiv.org/pdf/1511.01432v1.pdf
https://github.com/Zehui127/SQUAD_BERT
false
false
true
tf
https://paperswithcode.com/paper/weird-faccts-how-western-educated
WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?
2305.06415
https://arxiv.org/abs/2305.06415v1
https://arxiv.org/pdf/2305.06415v1.pdf
https://github.com/aliakbars/weird-facct
true
true
false
none
https://paperswithcode.com/paper/hypernetworks
HyperNetworks
1609.09106
http://arxiv.org/abs/1609.09106v4
http://arxiv.org/pdf/1609.09106v4.pdf
https://github.com/gahaalt/continual-learning-overview
false
false
true
tf
https://paperswithcode.com/paper/three-scenarios-for-continual-learning
Three scenarios for continual learning
1904.07734
http://arxiv.org/abs/1904.07734v1
http://arxiv.org/pdf/1904.07734v1.pdf
https://github.com/gahaalt/continual-learning-overview
false
false
true
tf
https://paperswithcode.com/paper/detecting-dga-domains-with-recurrent-neural
Detecting DGA domains with recurrent neural networks and side information
1810.02023
https://arxiv.org/abs/1810.02023v2
https://arxiv.org/pdf/1810.02023v2.pdf
https://github.com/alistairwgillespie/deep_dga_detection
false
false
true
pytorch
https://paperswithcode.com/paper/accelerating-the-super-resolution
Accelerating the Super-Resolution Convolutional Neural Network
1608.00367
http://arxiv.org/abs/1608.00367v1
http://arxiv.org/pdf/1608.00367v1.pdf
https://github.com/yjn870/FSRCNN-pytorch
false
false
true
pytorch
https://paperswithcode.com/paper/deep-learning-with-topological-signatures
Deep Learning with Topological Signatures
1707.04041
http://arxiv.org/abs/1707.04041v3
http://arxiv.org/pdf/1707.04041v3.pdf
https://github.com/billy-mosse/spiderman
false
false
true
none
https://paperswithcode.com/paper/sql-rank-a-listwise-approach-to-collaborative
SQL-Rank: A Listwise Approach to Collaborative Ranking
1803.00114
http://arxiv.org/abs/1803.00114v3
http://arxiv.org/pdf/1803.00114v3.pdf
https://github.com/wuliwei9278/SQL-Rank
true
true
false
none
https://paperswithcode.com/paper/drop-a-reading-comprehension-benchmark
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
1903.00161
http://arxiv.org/abs/1903.00161v2
http://arxiv.org/pdf/1903.00161v2.pdf
https://github.com/m3yrin/naqanet_notebook
false
false
true
none
https://paperswithcode.com/paper/on-finding-gray-pixels
On Finding Gray Pixels
1901.03198
https://arxiv.org/abs/1901.03198v3
https://arxiv.org/pdf/1901.03198v3.pdf
https://github.com/mahmoudnafifi/SIIE
false
false
true
tf
https://paperswithcode.com/paper/efficient-selection-of-predictive-biomarkers
Efficient selection of predictive biomarkers for individual treatment selection
1905.01582
https://arxiv.org/abs/1905.01582v1
https://arxiv.org/pdf/1905.01582v1.pdf
https://github.com/sshonosuke/SB-ITS
false
false
true
none
https://paperswithcode.com/paper/a-hierarchical-model-of-non-homogeneous
A hierarchical model of non-homogeneous Poisson processes for Twitter retweets
1802.01987
http://arxiv.org/abs/1802.01987v2
http://arxiv.org/pdf/1802.01987v2.pdf
https://github.com/clement-lee/hybridProcess
false
false
true
none
https://paperswithcode.com/paper/discontinuous-constituent-parsing-with
Discontinuous Constituent Parsing with Pointer Networks
2002.01824
https://arxiv.org/abs/2002.01824v1
https://arxiv.org/pdf/2002.01824v1.pdf
https://github.com/danifg/DiscoPointer
true
true
false
pytorch
https://paperswithcode.com/paper/wikihow-a-large-scale-text-summarization
WikiHow: A Large Scale Text Summarization Dataset
1810.09305
http://arxiv.org/abs/1810.09305v1
http://arxiv.org/pdf/1810.09305v1.pdf
https://github.com/LubdaMax/Data-Science-1
false
false
true
tf
https://paperswithcode.com/paper/computing-stable-models-of-normal-logic
Computing Stable Models of Normal Logic Programs Without Grounding
1709.00501
https://arxiv.org/abs/1709.00501v1
https://arxiv.org/pdf/1709.00501v1.pdf
https://github.com/sarat-chandra-varanasi/pysasp
false
false
true
none
https://paperswithcode.com/paper/unsupervised-domain-adaptation-through-self-1
Unsupervised Domain Adaptation through Self-Supervision
1909.11825
https://arxiv.org/abs/1909.11825v2
https://arxiv.org/pdf/1909.11825v2.pdf
https://github.com/Jinsung-Jeon/DomainAdaptation
false
false
true
pytorch
https://paperswithcode.com/paper/joint-learning-of-the-embedding-of-words-and
Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
1601.01343
http://arxiv.org/abs/1601.01343v4
http://arxiv.org/pdf/1601.01343v4.pdf
https://github.com/wikipedia2vec/wikipedia2vec
false
false
true
none
https://paperswithcode.com/paper/high-quality-monocular-depth-estimation-via
High Quality Monocular Depth Estimation via Transfer Learning
1812.11941
http://arxiv.org/abs/1812.11941v2
http://arxiv.org/pdf/1812.11941v2.pdf
https://github.com/KarthikGangadhar/depth-estimation
false
false
true
tf
https://paperswithcode.com/paper/towards-a-general-purpose-cnn-for-long-range
Towards a General Purpose CNN for Long Range Dependencies in $N$D
2206.03398
https://arxiv.org/abs/2206.03398v2
https://arxiv.org/pdf/2206.03398v2.pdf
https://github.com/david-knigge/ccnn
true
true
true
pytorch
https://paperswithcode.com/paper/axiomatic-ranking-of-network-role-similarity
Axiomatic Ranking of Network Role Similarity
1102.3937
https://arxiv.org/abs/1102.3937v2
https://arxiv.org/pdf/1102.3937v2.pdf
https://github.com/abhishekmaha23/RoleSim_python
false
false
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/mike-a-yen/date-translation
false
false
true
pytorch
https://paperswithcode.com/paper/explainable-neural-computation-via-stack
Explainable Neural Computation via Stack Neural Module Networks
1807.08556
http://arxiv.org/abs/1807.08556v3
http://arxiv.org/pdf/1807.08556v3.pdf
https://github.com/ronghanghu/snmn
false
false
true
tf
https://paperswithcode.com/paper/dftatom-a-robust-and-general-schrodinger-and
dftatom: A robust and general Schrödinger and Dirac solver for atomic structure calculations
1209.1752
http://arxiv.org/abs/1209.1752v2
http://arxiv.org/pdf/1209.1752v2.pdf
https://github.com/certik/dftatom
false
false
true
tf
https://paperswithcode.com/paper/friendship-paradox-biases-perceptions-in
Friendship Paradox Biases Perceptions in Directed Networks
1905.05286
https://arxiv.org/abs/1905.05286v1
https://arxiv.org/pdf/1905.05286v1.pdf
https://github.com/ninoch/perception_bias
true
false
true
none
https://paperswithcode.com/paper/local-global-fusion-network-for-video-super
Local-Global Fusion Network for Video Super-Resolution
null
https://ieeexplore.ieee.org/document/9203860/authors#authors
https://ieeexplore.ieee.org/document/9203860/authors#authors
https://github.com/BIOINSu/LGFN
false
true
false
pytorch
https://paperswithcode.com/paper/learning-new-physics-from-a-machine
Learning New Physics from a Machine
1806.02350
https://arxiv.org/abs/1806.02350v1
https://arxiv.org/pdf/1806.02350v1.pdf
https://github.com/gvlos/LNPFM
false
false
true
none
https://paperswithcode.com/paper/on-network-design-spaces-for-visual
On Network Design Spaces for Visual Recognition
1905.13214
https://arxiv.org/abs/1905.13214v1
https://arxiv.org/pdf/1905.13214v1.pdf
https://github.com/tuggeluk/pycls
false
false
true
pytorch
https://paperswithcode.com/paper/unsupervised-feature-learning-via-non
Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination
1805.01978
http://arxiv.org/abs/1805.01978v1
http://arxiv.org/pdf/1805.01978v1.pdf
https://github.com/zhirongw/lemniscate.pytorch
true
true
true
pytorch
https://paperswithcode.com/paper/towards-k-means-friendly-spaces-simultaneous
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
1610.04794
http://arxiv.org/abs/1610.04794v2
http://arxiv.org/pdf/1610.04794v2.pdf
https://github.com/XiaoxiangLin/DCN-keras
false
false
true
none
https://paperswithcode.com/paper/generative-adversarial-networks
Generative Adversarial Networks
1406.2661
https://arxiv.org/abs/1406.2661v1
https://arxiv.org/pdf/1406.2661v1.pdf
https://github.com/catalyst-team/gan
false
false
true
pytorch
https://paperswithcode.com/paper/modeling-and-control-of-a-reconfigurable
Modeling and Control of a Reconfigurable Photonic Circuit using Deep Learning
1907.08023
https://arxiv.org/abs/1907.08023v1
https://arxiv.org/pdf/1907.08023v1.pdf
https://github.com/akramyoussry/GRUBI
true
true
true
tf
https://paperswithcode.com/paper/a-very-low-resource-language-speech-corpus
A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments
1710.03501
http://arxiv.org/abs/1710.03501v3
http://arxiv.org/pdf/1710.03501v3.pdf
https://github.com/mzboito/mmboshi
false
false
true
none
https://paperswithcode.com/paper/empirical-evaluation-of-scoring-functions-for
Empirical evaluation of scoring functions for Bayesian network model selection
null
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-S15-S14#Abs1
https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-13-S15-S14
https://github.com/miladce/Bayesian-network-learning
false
false
false
none
https://paperswithcode.com/paper/hierarchical-human-parsing-with-typed-part
Hierarchical Human Parsing with Typed Part-Relation Reasoning
2003.04845
https://arxiv.org/abs/2003.04845v2
https://arxiv.org/pdf/2003.04845v2.pdf
https://github.com/hlzhu09/Hierarchical-Human-Parsing
true
true
false
pytorch
https://paperswithcode.com/paper/continuous-control-with-deep-reinforcement
Continuous control with deep reinforcement learning
1509.02971
https://arxiv.org/abs/1509.02971v6
https://arxiv.org/pdf/1509.02971v6.pdf
https://github.com/prajwalgatti/DRL-Continuous-Control
false
false
true
none
https://paperswithcode.com/paper/fine-grained-visual-classification-via
Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches
2003.03836
https://arxiv.org/abs/2003.03836v3
https://arxiv.org/pdf/2003.03836v3.pdf
https://github.com/RuoyiDu/PMG-Progressive-Multi-Granularity-Training
true
true
false
pytorch
https://paperswithcode.com/paper/mfes-hb-efficient-hyperband-with-multi
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
2012.03011
https://arxiv.org/abs/2012.03011v2
https://arxiv.org/pdf/2012.03011v2.pdf
https://github.com/thomas-young-2013/open-box
false
false
true
none
https://paperswithcode.com/paper/openbox-a-generalized-black-box-optimization
OpenBox: A Generalized Black-box Optimization Service
2106.00421
https://arxiv.org/abs/2106.00421v3
https://arxiv.org/pdf/2106.00421v3.pdf
https://github.com/thomas-young-2013/open-box
false
false
true
none
https://paperswithcode.com/paper/spotting-macro-and-micro-expression-intervals
Spotting Macro- and Micro-expression Intervals in Long Video Sequences
1912.11985
https://arxiv.org/abs/1912.11985v3
https://arxiv.org/pdf/1912.11985v3.pdf
https://github.com/HeyingGithub/Baseline-project-for-MEGC2020_spotting
true
true
true
none
https://paperswithcode.com/paper/keyfilter-aware-real-time-uav-object-tracking
Keyfilter-Aware Real-Time UAV Object Tracking
2003.05218
https://arxiv.org/abs/2003.05218v1
https://arxiv.org/pdf/2003.05218v1.pdf
https://github.com/vision4robotics/KAOT-tracker
true
true
false
none
https://paperswithcode.com/paper/energy-aware-coverage-planning-for
Energy-Aware Coverage Planning for Heterogeneous Multi-Robot System
2411.02230
https://arxiv.org/abs/2411.02230v1
https://arxiv.org/pdf/2411.02230v1.pdf
https://github.com/herolab-uga/energy-aware-coverage
true
false
true
none
https://paperswithcode.com/paper/advances-in-collaborative-filtering-and
Advances in Collaborative Filtering and Ranking
2002.12312
https://arxiv.org/abs/2002.12312v1
https://arxiv.org/pdf/2002.12312v1.pdf
https://github.com/wuliwei9278/SQL-Rank
true
true
false
none
https://paperswithcode.com/paper/a-clockwork-rnn
A Clockwork RNN
1402.3511
http://arxiv.org/abs/1402.3511v1
http://arxiv.org/pdf/1402.3511v1.pdf
https://github.com/html1101/Science-Fair-2019-2020
false
false
true
tf
https://paperswithcode.com/paper/approximating-network-centrality-measures
Approximating Network Centrality Measures Using Node Embedding and Machine Learning
2006.16392
https://arxiv.org/abs/2006.16392v4
https://arxiv.org/pdf/2006.16392v4.pdf
https://github.com/MatheusMRFM/NCA-GE
true
true
false
tf
https://paperswithcode.com/paper/power-law-distributions-in-empirical-data
Power-law distributions in empirical data
0706.1062
http://arxiv.org/abs/0706.1062v2
http://arxiv.org/pdf/0706.1062v2.pdf
https://github.com/jlapeyre/MaximumLikelihoodPower.jl
false
false
true
none
https://paperswithcode.com/paper/class-balanced-loss-based-on-effective-number
Class-Balanced Loss Based on Effective Number of Samples
1901.05555
http://arxiv.org/abs/1901.05555v1
http://arxiv.org/pdf/1901.05555v1.pdf
https://github.com/feidfoe/AdjustBnd4Imbalance
false
false
true
pytorch
https://paperswithcode.com/paper/fast-context-adaptation-via-meta-learning
Fast Context Adaptation via Meta-Learning
1810.03642
https://arxiv.org/abs/1810.03642v4
https://arxiv.org/pdf/1810.03642v4.pdf
https://github.com/lmzintgraf/cavia
true
true
true
pytorch
https://paperswithcode.com/paper/dual-attention-guided-dropblock-module-for
Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization
2003.04719
https://arxiv.org/abs/2003.04719v3
https://arxiv.org/pdf/2003.04719v3.pdf
https://github.com/cpuimage/DualAttentionGuidedDropout
false
false
true
tf
https://paperswithcode.com/paper/can-connected-autonomous-vehicles-really
Can Connected Autonomous Vehicles really improve mixed traffic efficiency in realistic scenarios?
2107.03078
https://arxiv.org/abs/2107.03078v2
https://arxiv.org/pdf/2107.03078v2.pdf
https://github.com/gargmohit24/ITSC_2021
true
true
false
none
https://paperswithcode.com/paper/contrastive-multiview-coding
Contrastive Multiview Coding
1906.05849
https://arxiv.org/abs/1906.05849v5
https://arxiv.org/pdf/1906.05849v5.pdf
https://github.com/szq0214/Rethinking-Image-Mixture-for-Unsupervised-Learning
false
false
true
pytorch
https://paperswithcode.com/paper/structural-regularities-in-text-based-entity
Structural Regularities in Text-based Entity Vector Spaces
1707.07930
http://arxiv.org/abs/1707.07930v1
http://arxiv.org/pdf/1707.07930v1.pdf
https://github.com/cvangysel/SERT
true
true
true
none
https://paperswithcode.com/paper/network-modelling-of-topological-domains
Network modelling of topological domains using Hi-C data
1707.09587
http://arxiv.org/abs/1707.09587v2
http://arxiv.org/pdf/1707.09587v2.pdf
https://github.com/zhongmicai/Hic_tools_collection
false
false
true
tf
https://paperswithcode.com/paper/pt2pc-learning-to-generate-3d-point-cloud
PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions
2003.08624
https://arxiv.org/abs/2003.08624v2
https://arxiv.org/pdf/2003.08624v2.pdf
https://github.com/daerduoCarey/pt2pc
false
false
true
pytorch
https://paperswithcode.com/paper/zero-shot-learning-a-comprehensive-evaluation
Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly
1707.00600
https://arxiv.org/abs/1707.00600v4
https://arxiv.org/pdf/1707.00600v4.pdf
https://github.com/vkverma01/Zero-Shot
false
false
true
none
https://paperswithcode.com/paper/robrose-a-robust-approach-for-dealing-with
robROSE: A robust approach for dealing with imbalanced data in fraud detection
2003.11915
https://arxiv.org/abs/2003.11915v1
https://arxiv.org/pdf/2003.11915v1.pdf
https://github.com/SebastiaanHoppner/robROSE
true
true
true
none
https://paperswithcode.com/paper/testing-of-deep-reinforcement-learning-agents
Testing of Deep Reinforcement Learning Agents with Surrogate Models
2305.12751
https://arxiv.org/abs/2305.12751v2
https://arxiv.org/pdf/2305.12751v2.pdf
https://github.com/matteobiagiola/drl-testing-experiments
true
true
false
none
https://paperswithcode.com/paper/faster-fast-tensor-completion-with-nonconvex
FasTer: Fast Tensor Completion with Nonconvex Regularization
1807.08725
http://arxiv.org/abs/1807.08725v3
http://arxiv.org/pdf/1807.08725v3.pdf
https://github.com/quanmingyao/FasTer
false
false
true
none
https://paperswithcode.com/paper/chatgpt-in-the-context-of-precision
ChatGPT in the context of precision agriculture data analytics
2311.06390
https://arxiv.org/abs/2311.06390v1
https://arxiv.org/pdf/2311.06390v1.pdf
https://github.com/potamitis123/chatgpt-in-the-context-of-precision-agriculture-data-analytics
true
true
false
none
https://paperswithcode.com/paper/fd-gan-pose-guided-feature-distilling-gan-for
FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
1810.02936
http://arxiv.org/abs/1810.02936v2
http://arxiv.org/pdf/1810.02936v2.pdf
https://github.com/NVlabs/DG-Net
false
false
false
pytorch
https://paperswithcode.com/paper/accurate-large-minibatch-sgd-training
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
1706.02677
http://arxiv.org/abs/1706.02677v2
http://arxiv.org/pdf/1706.02677v2.pdf
https://github.com/kenziyuliu/ms-g3d
false
false
true
pytorch