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/construction-of-large-scale-english-verbal
Construction of Large-scale English Verbal Multiword Expression Annotated Corpus
null
https://aclanthology.org/L18-1396
https://aclanthology.org/L18-1396.pdf
https://github.com/naist-cl-parsing/Verbal-MWE-annotations
true
true
false
none
https://paperswithcode.com/paper/astronomaly-protege-discovery-through-human
Astronomaly Protege: Discovery Through Human-Machine Collaboration
2411.04188
https://arxiv.org/abs/2411.04188v3
https://arxiv.org/pdf/2411.04188v3.pdf
https://github.com/michellelochner/mgcls.protege
true
true
true
none
https://paperswithcode.com/paper/traffic4cast-large-scale-traffic-prediction
Traffic4cast -- Large-scale Traffic Prediction using 3DResNet and Sparse-UNet
2111.05990
https://arxiv.org/abs/2111.05990v1
https://arxiv.org/pdf/2111.05990v1.pdf
https://github.com/resuly/traffic4cast-2021
true
true
true
pytorch
https://paperswithcode.com/paper/a-deep-generative-framework-for-paraphrase
A Deep Generative Framework for Paraphrase Generation
1709.05074
http://arxiv.org/abs/1709.05074v1
http://arxiv.org/pdf/1709.05074v1.pdf
https://github.com/arvind385801/paraphrasegen
false
false
true
pytorch
https://paperswithcode.com/paper/frank-wolfe-methods-with-an-unbounded
Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning
2012.15361
https://arxiv.org/abs/2012.15361v2
https://arxiv.org/pdf/2012.15361v2.pdf
https://github.com/wanghaoyue123/frank-wolfe-with-unbounded-constraints
true
true
false
none
https://paperswithcode.com/paper/deep-reinforcement-learning-with-double-q
Deep Reinforcement Learning with Double Q-learning
1509.06461
http://arxiv.org/abs/1509.06461v3
http://arxiv.org/pdf/1509.06461v3.pdf
https://github.com/ianlimle/ItsMeMario
false
false
true
pytorch
https://paperswithcode.com/paper/a-factored-neural-network-model-for
A Factored Neural Network Model for Characterizing Online Discussions in Vector Space
null
https://aclanthology.org/D17-1243
https://aclanthology.org/D17-1243.pdf
https://github.com/hao-cheng/factored_neural
true
true
false
tf
https://paperswithcode.com/paper/implicit-neural-representations-with-periodic
Implicit Neural Representations with Periodic Activation Functions
2006.09661
https://arxiv.org/abs/2006.09661v1
https://arxiv.org/pdf/2006.09661v1.pdf
https://github.com/TalFurman/Implict_neural_representation_of_images
false
false
true
pytorch
https://paperswithcode.com/paper/what-were-they-thinking-pharmacologic-priors
What Were They Thinking? Pharmacologic priors implicit in a choice of 3+3 dose-escalation design
2012.05301
https://arxiv.org/abs/2012.05301v2
https://arxiv.org/pdf/2012.05301v2.pdf
https://github.com/dcnorris/precautionary
false
false
true
none
https://paperswithcode.com/paper/retrospective-analysis-of-a-fatal-dose
Retrospective analysis of a fatal dose-finding trial
2004.12755
http://arxiv.org/abs/2004.12755v1
http://arxiv.org/pdf/2004.12755v1.pdf
https://github.com/dcnorris/precautionary
false
false
true
none
https://paperswithcode.com/paper/need-for-speed-a-benchmark-for-higher-frame
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking
1703.05884
http://arxiv.org/abs/1703.05884v2
http://arxiv.org/pdf/1703.05884v2.pdf
https://github.com/susomena/DeepSlowMotion
false
false
true
tf
https://paperswithcode.com/paper/feature-importance-aware-transferable
Feature Importance-aware Transferable Adversarial Attacks
2107.14185
https://arxiv.org/abs/2107.14185v3
https://arxiv.org/pdf/2107.14185v3.pdf
https://github.com/ZOMIN28/FIA-pytorch
false
false
false
pytorch
https://paperswithcode.com/paper/flipda-effective-and-robust-data-augmentation
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning
2108.06332
https://arxiv.org/abs/2108.06332v2
https://arxiv.org/pdf/2108.06332v2.pdf
https://github.com/zhouj8553/flipda
true
true
false
pytorch
https://paperswithcode.com/paper/robustfill-neural-program-learning-under
RobustFill: Neural Program Learning under Noisy I/O
1703.07469
http://arxiv.org/abs/1703.07469v1
http://arxiv.org/pdf/1703.07469v1.pdf
https://github.com/amitz25/PCCoder
false
false
true
pytorch
https://paperswithcode.com/paper/overfitting-the-data-compact-neural-video
Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation
2108.08202
https://arxiv.org/abs/2108.08202v2
https://arxiv.org/pdf/2108.08202v2.pdf
https://github.com/neural-video-delivery/cafm-pytorch-iccv2021
true
true
false
pytorch
https://paperswithcode.com/paper/out-of-distribution-detection-using-outlier
Out-of-Distribution Detection Using Outlier Detection Methods
2108.08218
https://arxiv.org/abs/2108.08218v2
https://arxiv.org/pdf/2108.08218v2.pdf
https://github.com/jandiers/ood-detection
true
true
false
tf
https://paperswithcode.com/paper/offline-meta-reinforcement-learning-with-1
Offline Meta-Reinforcement Learning with Online Self-Supervision
2107.03974
https://arxiv.org/abs/2107.03974v4
https://arxiv.org/pdf/2107.03974v4.pdf
https://github.com/anair13/bullet-manipulation-affordances
false
false
true
none
https://paperswithcode.com/paper/model-change-active-learning-in-graph-based
Model-Change Active Learning in Graph-Based Semi-Supervised Learning
2110.07739
https://arxiv.org/abs/2110.07739v2
https://arxiv.org/pdf/2110.07739v2.pdf
https://github.com/millerk22/model-change-paper
true
true
false
none
https://paperswithcode.com/paper/monotonic-chunkwise-attention
Monotonic Chunkwise Attention
1712.05382
http://arxiv.org/abs/1712.05382v2
http://arxiv.org/pdf/1712.05382v2.pdf
https://github.com/craffel/mocha
true
true
true
tf
https://paperswithcode.com/paper/what-can-i-do-here-learning-new-skills-by
What Can I Do Here? Learning New Skills by Imagining Visual Affordances
2106.00671
https://arxiv.org/abs/2106.00671v2
https://arxiv.org/pdf/2106.00671v2.pdf
https://github.com/anair13/bullet-manipulation-affordances
false
false
true
none
https://paperswithcode.com/paper/keynet-keypoint-detection-by-handcrafted-and
Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters
1904.00889
https://arxiv.org/abs/1904.00889v3
https://arxiv.org/pdf/1904.00889v3.pdf
https://github.com/bluedream1121/Key.Net_PyTorch
false
false
true
pytorch
https://paperswithcode.com/paper/plan-attend-generate-character-level-neural-1
Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder
1706.05087
http://arxiv.org/abs/1706.05087v2
http://arxiv.org/pdf/1706.05087v2.pdf
https://github.com/nyu-dl/dl4mt-cdec
true
true
false
none
https://paperswithcode.com/paper/automated-coronal-hole-identification-via
Automated Coronal Hole Identification via Multi-Thermal Intensity Segmentation
1711.11476
http://arxiv.org/abs/1711.11476v1
http://arxiv.org/pdf/1711.11476v1.pdf
https://github.com/GartontT/CHIMERA
false
false
true
none
https://paperswithcode.com/paper/benchmarking-relief-based-feature-selection
Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining
1711.08477
http://arxiv.org/abs/1711.08477v2
http://arxiv.org/pdf/1711.08477v2.pdf
https://github.com/EpistasisLab/ReBATE
true
true
true
none
https://paperswithcode.com/paper/readers-vs-writers-vs-texts-coping-with
Readers vs. Writers vs. Texts: Coping with Different Perspectives of Text Understanding in Emotion Annotation
null
https://aclanthology.org/W17-0801
https://aclanthology.org/W17-0801.pdf
https://github.com/JULIELab/EmoBank
true
true
false
none
https://paperswithcode.com/paper/automating-the-search-for-a-patents-prior-art
Automating the search for a patent's prior art with a full text similarity search
1901.03136
http://arxiv.org/abs/1901.03136v2
http://arxiv.org/pdf/1901.03136v2.pdf
https://github.com/helmersl/patent_similarity_search
true
true
false
tf
https://paperswithcode.com/paper/continuous-cutting-plane-algorithms-in
Continuous cutting plane algorithms in integer programming
2204.09122
https://arxiv.org/abs/2204.09122v3
https://arxiv.org/pdf/2204.09122v3.pdf
https://github.com/dchetelat/subadditive
true
true
false
pytorch
https://paperswithcode.com/paper/adaptive-convolution-kernel-for-artificial
Adaptive Convolution Kernel for Artificial Neural Networks
2009.06385
https://arxiv.org/abs/2009.06385v1
https://arxiv.org/pdf/2009.06385v1.pdf
https://github.com/btekgit/AdaptiveCNN
true
true
true
tf
https://paperswithcode.com/paper/shield-fast-practical-defense-and-vaccination
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
1802.06816
http://arxiv.org/abs/1802.06816v1
http://arxiv.org/pdf/1802.06816v1.pdf
https://github.com/Yuxin33/unmask
false
false
true
pytorch
https://paperswithcode.com/paper/unmask-adversarial-detection-and-defense
UnMask: Adversarial Detection and Defense Through Robust Feature Alignment
2002.09576
https://arxiv.org/abs/2002.09576v2
https://arxiv.org/pdf/2002.09576v2.pdf
https://github.com/Yuxin33/unmask
false
false
true
pytorch
https://paperswithcode.com/paper/the-numerics-of-gans
The Numerics of GANs
1705.10461
http://arxiv.org/abs/1705.10461v3
http://arxiv.org/pdf/1705.10461v3.pdf
https://github.com/nhynes/abc
false
false
true
pytorch
https://paperswithcode.com/paper/convolutional-neural-networks-for-sentence
Convolutional Neural Networks for Sentence Classification
1408.5882
http://arxiv.org/abs/1408.5882v2
http://arxiv.org/pdf/1408.5882v2.pdf
https://github.com/ddajing/multilayer-cnn-text-classification
false
false
true
tf
https://paperswithcode.com/paper/task-aware-information-routing-from-common
Task-Aware Information Routing from Common Representation Space in Lifelong Learning
2302.11346
https://arxiv.org/abs/2302.11346v1
https://arxiv.org/pdf/2302.11346v1.pdf
https://github.com/neurai-lab/tamil
true
true
false
pytorch
https://paperswithcode.com/paper/efficientvit-enhanced-linear-attention-for
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
2205.14756
https://arxiv.org/abs/2205.14756v6
https://arxiv.org/pdf/2205.14756v6.pdf
https://github.com/2023-MindSpore-4/Code10/tree/main/Efficientnet
false
false
false
mindspore
https://paperswithcode.com/paper/developing-a-unified-pipeline-for-large-scale-2
Developing a unified pipeline for large-scale structure data analysis with angular power spectra -- III. Implementing the multi-tracer technique to constrain neutrino masses
2009.05584
http://arxiv.org/abs/2009.05584v2
http://arxiv.org/pdf/2009.05584v2.pdf
https://github.com/ktanidis/Modified_CosmoSIS_for_galaxy_number_count_angular_power_spectra
true
true
true
none
https://paperswithcode.com/paper/plato-xl-exploring-the-large-scale-pre
PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation
2109.09519
https://arxiv.org/abs/2109.09519v2
https://arxiv.org/pdf/2109.09519v2.pdf
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/plato-xl
false
false
false
paddle
https://paperswithcode.com/paper/an-upper-bound-for-the-number-of-chess
An upper bound for the number of chess diagrams without promotion
2112.09386
https://arxiv.org/abs/2112.09386v2
https://arxiv.org/pdf/2112.09386v2.pdf
https://github.com/DanielGourion/ChessDiagrams
true
false
false
none
https://paperswithcode.com/paper/learning-to-diversify-for-single-domain
Learning to Diversify for Single Domain Generalization
2108.11726
https://arxiv.org/abs/2108.11726v3
https://arxiv.org/pdf/2108.11726v3.pdf
https://github.com/busername/learning_to_diversify
true
true
true
pytorch
https://paperswithcode.com/paper/njoy-ncrystal-an-open-source-tool-for
NJOY+NCrystal: an open-source tool for creating thermal neutron scattering libraries
2108.11737
https://arxiv.org/abs/2108.11737v2
https://arxiv.org/pdf/2108.11737v2.pdf
https://github.com/highness-eu/njoy-ncrystal-library
true
true
false
none
https://paperswithcode.com/paper/fully-convolutional-networks-for-semantic
Fully Convolutional Networks for Semantic Segmentation
1605.06211
http://arxiv.org/abs/1605.06211v1
http://arxiv.org/pdf/1605.06211v1.pdf
https://github.com/2023-MindSpore-4/Code10/tree/main/FCN8s
false
false
false
mindspore
https://paperswithcode.com/paper/micromechanical-fatigue-experiments-for
Micromechanical fatigue experiments for validation of microstructure-sensitive fatigue simulation models
2112.04342
https://arxiv.org/abs/2112.04342v1
https://arxiv.org/pdf/2112.04342v1.pdf
https://github.com/boschresearch/vitemi
true
true
false
none
https://paperswithcode.com/paper/lyra-a-benchmark-for-turducken-style-code
Lyra: A Benchmark for Turducken-Style Code Generation
2108.12144
https://arxiv.org/abs/2108.12144v3
https://arxiv.org/pdf/2108.12144v3.pdf
https://github.com/liangqingyuan/lyra
true
true
true
pytorch
https://paperswithcode.com/paper/exploiting-anticommutation-in-hamiltonian
Exploiting anticommutation in Hamiltonian simulation
2103.07988
https://arxiv.org/abs/2103.07988v2
https://arxiv.org/pdf/2103.07988v2.pdf
https://github.com/zhaoqthu/anticommuHamiltonian
true
false
false
none
https://paperswithcode.com/paper/an-efficient-lstm-neural-network-based
An Efficient LSTM Neural Network-Based Framework for Vessel Location Forecasting
null
https://doi.org/10.1109/TITS.2023.3247993
https://scholar.google.com/scholar_url?url=https://ieeexplore.ieee.org/iel7/6979/4358928/10073952.pdf%3Fcasa_token%3Dn7JSzcTZI-YAAAAA:tfrTPUg5tvRJlwMG3EvMTr_TsZw-QlE71AFzpGLJcTU3E5gavmIam3ei0d3vwT7SbbfIW8Rd5Q&hl=el&sa=T&oi=ucasa&ct=ucasa&ei=cT8xZKqfF_6Sy9YPxfKx8A0&scisig=AJ9-iYufiQelQanCZCKIqoqoN2fr
https://github.com/eva-chon/VLF_VRF
false
false
false
tf
https://paperswithcode.com/paper/emotion-prediction-oriented-method-with
Emotion Prediction Oriented method with Multiple Supervisions for Emotion-Cause Pair Extraction
2302.12417
https://arxiv.org/abs/2302.12417v1
https://arxiv.org/pdf/2302.12417v1.pdf
https://github.com/lemei/epo-ecpe
true
true
true
pytorch
https://paperswithcode.com/paper/mlp-mixer-an-all-mlp-architecture-for-vision
MLP-Mixer: An all-MLP Architecture for Vision
2105.01601
https://arxiv.org/abs/2105.01601v4
https://arxiv.org/pdf/2105.01601v4.pdf
https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/MLP-Mixer
false
false
false
paddle
https://paperswithcode.com/paper/a-new-procedure-for-selective-inference-with
SIGLE: a valid procedure for Selective Inference with the Generalized Linear Lasso
2203.15348
https://arxiv.org/abs/2203.15348v3
https://arxiv.org/pdf/2203.15348v3.pdf
https://github.com/quentin-duchemin/sigle
true
true
false
none
https://paperswithcode.com/paper/attention-is-all-you-need
Attention Is All You Need
1706.03762
https://arxiv.org/abs/1706.03762v7
https://arxiv.org/pdf/1706.03762v7.pdf
https://github.com/Bhavnicksm/vanilla-transformer-jax
false
false
true
jax
https://paperswithcode.com/paper/topic-aware-abstractive-text-summarization
Topic-Guided Abstractive Text Summarization: a Joint Learning Approach
2010.10323
https://arxiv.org/abs/2010.10323v2
https://arxiv.org/pdf/2010.10323v2.pdf
https://github.com/chz816/tas
true
true
false
pytorch
https://paperswithcode.com/paper/federated-reconnaissance-efficient
Federated Reconnaissance: Efficient, Distributed, Class-Incremental Learning
2109.00150
https://arxiv.org/abs/2109.00150v1
https://arxiv.org/pdf/2109.00150v1.pdf
https://github.com/ml4ai/fed-recon
true
true
true
pytorch
https://paperswithcode.com/paper/improving-multimodal-fusion-with-hierarchical
Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis
2109.00412
https://arxiv.org/abs/2109.00412v2
https://arxiv.org/pdf/2109.00412v2.pdf
https://github.com/declare-lab/multimodal-infomax
true
true
true
pytorch
https://paperswithcode.com/paper/torchaudio-building-blocks-for-audio-and
TorchAudio: Building Blocks for Audio and Speech Processing
2110.15018
https://arxiv.org/abs/2110.15018v2
https://arxiv.org/pdf/2110.15018v2.pdf
https://github.com/pytorch/audio
true
true
false
pytorch
https://paperswithcode.com/paper/modular-retrieval-for-generalization-and
Modular Retrieval for Generalization and Interpretation
2303.13419
https://arxiv.org/abs/2303.13419v1
https://arxiv.org/pdf/2303.13419v1.pdf
https://github.com/freedomintelligence/remop
true
true
true
pytorch
https://paperswithcode.com/paper/creative-diversity-patterns-in-the-creative
Creative Diversity: Patterns in the Creative Habits of ~10,000 People
2108.12759
https://arxiv.org/abs/2108.12759v2
https://arxiv.org/pdf/2108.12759v2.pdf
https://github.com/ericberlow/creative-diversity
true
false
false
none
https://paperswithcode.com/paper/have-i-done-enough-planning-or-should-i-plan
Have I done enough planning or should I plan more?
2201.00764
https://arxiv.org/abs/2201.00764v1
https://arxiv.org/pdf/2201.00764v1.pdf
https://github.com/reeche/planningamount
true
true
false
none
https://paperswithcode.com/paper/when-does-classical-chinese-help-quantifying
When Does Classical Chinese Help? Quantifying Cross-Lingual Transfer in Hanja and Kanbun
2411.04822
https://arxiv.org/abs/2411.04822v1
https://arxiv.org/pdf/2411.04822v1.pdf
https://github.com/seyoungsong/classical-chinese-transfer
true
false
true
none
https://paperswithcode.com/paper/the-newspaper-navigator-dataset-extracting
The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America
2005.01583
https://arxiv.org/abs/2005.01583v1
https://arxiv.org/pdf/2005.01583v1.pdf
https://github.com/parthasarathy-ss/newspaper-navigator
false
false
true
pytorch
https://paperswithcode.com/paper/online-generalized-method-of-moments-for-time
Online Generalized Method of Moments for Time Series
2502.00751
https://arxiv.org/abs/2502.00751v1
https://arxiv.org/pdf/2502.00751v1.pdf
https://github.com/hemanlmf/gmwm
true
true
false
none
https://paperswithcode.com/paper/bert-assisted-semantic-annotation-correction
BERT-Assisted Semantic Annotation Correction for Emotion-Related Questions
2204.00916
https://arxiv.org/abs/2204.00916v1
https://arxiv.org/pdf/2204.00916v1.pdf
https://github.com/abecode/emo20q
true
true
false
none
https://paperswithcode.com/paper/scalable-feature-matching-across-large-data
Scalable Feature Matching Across Large Data Collections
2101.02035
https://arxiv.org/abs/2101.02035v1
https://arxiv.org/pdf/2101.02035v1.pdf
https://github.com/ddegras/matchFeat
true
true
true
none
https://paperswithcode.com/paper/learning-phrase-representations-using-rnn
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
1406.1078
http://arxiv.org/abs/1406.1078v3
http://arxiv.org/pdf/1406.1078v3.pdf
https://github.com/mindspore-ai/models/tree/master/official/nlp/gru
false
false
false
mindspore
https://paperswithcode.com/paper/wenet-production-first-and-production-ready
WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit
2102.01547
https://arxiv.org/abs/2102.01547v5
https://arxiv.org/pdf/2102.01547v5.pdf
https://github.com/wenet-e2e/wenet
true
true
false
pytorch
https://paperswithcode.com/paper/timetraveler-reinforcement-learning-for
TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting
2109.04101
https://arxiv.org/abs/2109.04101v1
https://arxiv.org/pdf/2109.04101v1.pdf
https://github.com/jhl-hust/titer
true
true
false
pytorch
https://paperswithcode.com/paper/sampling-in-dirichlet-process-mixture-models
Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
2202.13312
https://arxiv.org/abs/2202.13312v1
https://arxiv.org/pdf/2202.13312v1.pdf
https://github.com/bgu-cs-vil/dpmmsubclustersstreaming.jl
true
true
false
none
https://paperswithcode.com/paper/consensus-learning-from-heterogeneous
Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering
2202.13140
https://arxiv.org/abs/2202.13140v1
https://arxiv.org/pdf/2202.13140v1.pdf
https://github.com/seongku-kang/concf_www22
true
true
false
pytorch
https://paperswithcode.com/paper/voxel-transformer-for-3d-object-detection
Voxel Transformer for 3D Object Detection
2109.02497
https://arxiv.org/abs/2109.02497v2
https://arxiv.org/pdf/2109.02497v2.pdf
https://github.com/PointsCoder/VOTR
false
false
false
pytorch
https://paperswithcode.com/paper/transreid-transformer-based-object-re
TransReID: Transformer-based Object Re-Identification
2102.04378
https://arxiv.org/abs/2102.04378v2
https://arxiv.org/pdf/2102.04378v2.pdf
https://github.com/darrishabh/coviprox
false
false
true
pytorch
https://paperswithcode.com/paper/very-deep-convolutional-networks-for-large
Very Deep Convolutional Networks for Large-Scale Image Recognition
1409.1556
http://arxiv.org/abs/1409.1556v6
http://arxiv.org/pdf/1409.1556v6.pdf
https://github.com/darrishabh/coviprox
false
false
true
pytorch
https://paperswithcode.com/paper/improved-latent-tree-induction-with-distant
Improved Latent Tree Induction with Distant Supervision via Span Constraints
2109.05112
https://arxiv.org/abs/2109.05112v2
https://arxiv.org/pdf/2109.05112v2.pdf
https://github.com/iesl/distantly-supervised-diora
true
true
false
pytorch
https://paperswithcode.com/paper/exploring-the-role-of-bert-token
Exploring the Role of BERT Token Representations to Explain Sentence Probing Results
2104.01477
https://arxiv.org/abs/2104.01477v2
https://arxiv.org/pdf/2104.01477v2.pdf
https://github.com/hmohebbi/explain-probing-results
true
true
true
none
https://paperswithcode.com/paper/total-recall-a-customized-continual-learning
Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers
2109.05186
https://arxiv.org/abs/2109.05186v2
https://arxiv.org/pdf/2109.05186v2.pdf
https://github.com/zhuang-li/cl_nsp
true
true
true
pytorch
https://paperswithcode.com/paper/squeezed-very-deep-convolutional-neural
Squeezed Very Deep Convolutional Neural Networks for Text Classification
1901.09821
http://arxiv.org/abs/1901.09821v1
http://arxiv.org/pdf/1901.09821v1.pdf
https://github.com/lazarotm/SVDCNN
false
true
false
pytorch
https://paperswithcode.com/paper/on-multi-layer-basis-pursuit-efficient
On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks
1806.00701
http://arxiv.org/abs/1806.00701v5
http://arxiv.org/pdf/1806.00701v5.pdf
https://github.com/Sulam-Group/ml-ista
false
false
true
pytorch
https://paperswithcode.com/paper/federated-learning-from-big-data-over
Federated Learning From Big Data Over Networks
2010.14159
https://arxiv.org/abs/2010.14159v1
https://arxiv.org/pdf/2010.14159v1.pdf
https://github.com/sahelyiyi/FederatedLearning
true
false
true
pytorch
https://paperswithcode.com/paper/fonbund-a-library-for-combining-cross-lingual
FonBund: A Library for Combining Cross-lingual Phonological Segment Data
null
https://aclanthology.org/L18-1353
https://aclanthology.org/L18-1353.pdf
https://github.com/googlei18n/language-resources
true
true
false
none
https://paperswithcode.com/paper/duluth-urop-at-semeval-2018-task-2
Duluth UROP at SemEval-2018 Task 2: Multilingual Emoji Prediction with Ensemble Learning and Oversampling
1805.10267
http://arxiv.org/abs/1805.10267v1
http://arxiv.org/pdf/1805.10267v1.pdf
https://github.com/shuningjin/SemEval2018-Task2-EmojiDetection
true
true
true
none
https://paperswithcode.com/paper/u-net-convolutional-networks-for-biomedical
U-Net: Convolutional Networks for Biomedical Image Segmentation
1505.04597
http://arxiv.org/abs/1505.04597v1
http://arxiv.org/pdf/1505.04597v1.pdf
https://github.com/adityarajsahu/UNet-Implementation
false
false
true
tf
https://paperswithcode.com/paper/hdr-image-reconstruction-from-a-single
HDR image reconstruction from a single exposure using deep CNNs
1710.07480
http://arxiv.org/abs/1710.07480v1
http://arxiv.org/pdf/1710.07480v1.pdf
https://github.com/mantiuk/pwcmp
true
true
false
none
https://paperswithcode.com/paper/voxelwise-nonlinear-regression-toolbox-for
Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling
1612.00667
http://arxiv.org/abs/1612.00667v3
http://arxiv.org/pdf/1612.00667v3.pdf
https://github.com/imatge-upc/VNeAT
true
true
false
none
https://paperswithcode.com/paper/deriving-consensus-for-multi-parallel-corpora
Deriving Consensus for Multi-Parallel Corpora: an English Bible Study
null
https://aclanthology.org/I17-2076
https://aclanthology.org/I17-2076.pdf
https://github.com/pitrack/monolign
true
true
false
none
https://paperswithcode.com/paper/a-general-optimization-framework-for-multi
A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence
null
https://aclanthology.org/C16-1024
https://aclanthology.org/C16-1024.pdf
https://github.com/UKPLab/coling2016-genetic-swarm-MDS
true
true
false
none
https://paperswithcode.com/paper/improving-low-resource-neural-machine
Improving Low-Resource Neural Machine Translation with Filtered Pseudo-Parallel Corpus
null
https://aclanthology.org/W17-5704
https://aclanthology.org/W17-5704.pdf
https://github.com/aizhanti/filtered-pseudo-parallel-corpora
true
true
false
none
https://paperswithcode.com/paper/resnet-with-one-neuron-hidden-layers-is-a
ResNet with one-neuron hidden layers is a Universal Approximator
1806.10909
http://arxiv.org/abs/1806.10909v2
http://arxiv.org/pdf/1806.10909v2.pdf
https://github.com/sivakon/resnet-approximator
false
false
true
none
https://paperswithcode.com/paper/fourier-pca-and-robust-tensor-decomposition
Fourier PCA and Robust Tensor Decomposition
1306.5825
http://arxiv.org/abs/1306.5825v5
http://arxiv.org/pdf/1306.5825v5.pdf
https://github.com/yingusxiaous/libFPCA
false
false
true
none
https://paperswithcode.com/paper/non-convex-global-minimization-and-false
Non-convex Global Minimization and False Discovery Rate Control for the TREX
1604.06815
http://arxiv.org/abs/1604.06815v2
http://arxiv.org/pdf/1604.06815v2.pdf
https://github.com/muellsen/TREX
true
true
false
none
https://paperswithcode.com/paper/pruning-convolutional-neural-networks-for
Pruning Convolutional Neural Networks for Resource Efficient Inference
1611.06440
http://arxiv.org/abs/1611.06440v2
http://arxiv.org/pdf/1611.06440v2.pdf
https://github.com/dongkwan-kim/Adaptive-Forgetting
false
false
true
tf
https://paperswithcode.com/paper/ssd-single-shot-multibox-detector
SSD: Single Shot MultiBox Detector
1512.02325
http://arxiv.org/abs/1512.02325v5
http://arxiv.org/pdf/1512.02325v5.pdf
https://github.com/GitHberChen/FCN-Pytorch
false
false
true
pytorch
https://paperswithcode.com/paper/automatic-skin-lesion-segmentation-on
Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging
1808.06759
http://arxiv.org/abs/1808.06759v1
http://arxiv.org/pdf/1808.06759v1.pdf
https://github.com/dipaco/mole-classification
true
true
false
none
https://paperswithcode.com/paper/detecting-gang-involved-escalation-on-social
Detecting Gang-Involved Escalation on Social Media Using Context
1809.03632
http://arxiv.org/abs/1809.03632v1
http://arxiv.org/pdf/1809.03632v1.pdf
https://github.com/serinachang5/contextifier
true
true
false
none
https://paperswithcode.com/paper/180602449
Joint Power Allocation in Interference-Limited Networks via Distributed Coordinated Learning
1806.02449
http://arxiv.org/abs/1806.02449v2
http://arxiv.org/pdf/1806.02449v2.pdf
https://github.com/roamiri/pa_intf_RL
false
false
true
none
https://paperswithcode.com/paper/dimspan-transactional-frequent-subgraph
DIMSpan - Transactional Frequent Subgraph Mining with Distributed In-Memory Dataflow Systems
1703.01910
http://arxiv.org/abs/1703.01910v1
http://arxiv.org/pdf/1703.01910v1.pdf
https://github.com/fuboertech/gradoop
false
false
true
none
https://paperswithcode.com/paper/rand-walk-a-latent-variable-model-approach-to
A Latent Variable Model Approach to PMI-based Word Embeddings
1502.03520
https://arxiv.org/abs/1502.03520v8
https://arxiv.org/pdf/1502.03520v8.pdf
https://github.com/LivNLP/Relational-Walk-for-Knowledge-Graphs
false
false
true
tf
https://paperswithcode.com/paper/proximal-policy-optimization-algorithms
Proximal Policy Optimization Algorithms
1707.06347
http://arxiv.org/abs/1707.06347v2
http://arxiv.org/pdf/1707.06347v2.pdf
https://github.com/sc2crazy/StarCrackRL
false
false
true
tf
https://paperswithcode.com/paper/fast-neural-architecture-search-of-compact
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
1810.10804
https://arxiv.org/abs/1810.10804v3
https://arxiv.org/pdf/1810.10804v3.pdf
https://github.com/drsleep/nas-segm-pytorch
true
true
true
pytorch
https://paperswithcode.com/paper/east-an-efficient-and-accurate-scene-text
EAST: An Efficient and Accurate Scene Text Detector
1704.03155
http://arxiv.org/abs/1704.03155v2
http://arxiv.org/pdf/1704.03155v2.pdf
https://github.com/BruceChanJianLe/Image-Text-Recognition
false
false
true
none
https://paperswithcode.com/paper/visual-interpretability-for-deep-learning-a
Visual Interpretability for Deep Learning: a Survey
1802.00614
http://arxiv.org/abs/1802.00614v2
http://arxiv.org/pdf/1802.00614v2.pdf
https://github.com/JepsonWong/CNN_Visualization
false
false
true
none
https://paperswithcode.com/paper/perturbative-gan-gan-with-perturbation-layers
Perturbative GAN: GAN with Perturbation Layers
1902.01514
http://arxiv.org/abs/1902.01514v1
http://arxiv.org/pdf/1902.01514v1.pdf
https://github.com/obake2ai/Obake-GAN
false
false
true
pytorch
https://paperswithcode.com/paper/ranger-a-fast-implementation-of-random
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
1508.04409
http://arxiv.org/abs/1508.04409v2
http://arxiv.org/pdf/1508.04409v2.pdf
https://github.com/mayer79/missRanger
false
false
true
none
https://paperswithcode.com/paper/deep-residual-learning-for-image-recognition
Deep Residual Learning for Image Recognition
1512.03385
http://arxiv.org/abs/1512.03385v1
http://arxiv.org/pdf/1512.03385v1.pdf
https://github.com/alililia/ms_extend/tree/main/gpu_resnet
false
false
false
mindspore
https://paperswithcode.com/paper/paying-attention-to-descriptions-generated-by
Paying Attention to Descriptions Generated by Image Captioning Models
1704.07434
http://arxiv.org/abs/1704.07434v3
http://arxiv.org/pdf/1704.07434v3.pdf
https://github.com/rakshithShetty/captionGAN
false
false
true
none