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

Dataset card Data Studio Files
xet
Community
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https://paperswithcode.com/paper/transformer-interpretability-beyond-attention
Transformer Interpretability Beyond Attention Visualization
2012.09838
https://arxiv.org/abs/2012.09838v2
https://arxiv.org/pdf/2012.09838v2.pdf
https://github.com/TiagoFilipeSousaGoncalves/survey-attention-medical-imaging
false
false
true
pytorch
https://paperswithcode.com/paper/optimal-vaccination-at-high-reproductive
Optimal vaccination at high reproductive numbers: sharp transitions and counter-intuitive allocations
2202.03909
https://arxiv.org/abs/2202.03909v1
https://arxiv.org/pdf/2202.03909v1.pdf
https://github.com/ngavish/highr0
true
true
false
none
https://paperswithcode.com/paper/spineparsenet-spine-parsing-for-volumetric-mr
SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework with Semantic Image Representation
null
https://ieeexplore.ieee.org/document/9201093
https://ieeexplore.ieee.org/document/9201093
https://github.com/pangshumao/SpineParseNet
false
false
false
pytorch
https://paperswithcode.com/paper/exploiting-anti-monotonicity-of-multi-label
Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules
1812.06833
http://arxiv.org/abs/1812.06833v1
http://arxiv.org/pdf/1812.06833v1.pdf
https://github.com/keelm/SeCo-MLC
true
true
true
none
https://paperswithcode.com/paper/efficient-discovery-of-expressive-multi-label
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
1908.06874
https://arxiv.org/abs/1908.06874v1
https://arxiv.org/pdf/1908.06874v1.pdf
https://github.com/keelm/SeCo-MLC
true
true
true
none
https://paperswithcode.com/paper/towards-demystifying-representation-learning-1
Towards Demystifying Representation Learning with Non-contrastive Self-supervision
2110.04947
https://arxiv.org/abs/2110.04947v2
https://arxiv.org/pdf/2110.04947v2.pdf
https://github.com/miszkur/SelfSupervisedLearning
false
false
true
tf
https://paperswithcode.com/paper/bootstrap-your-own-latent-a-new-approach-to
Bootstrap your own latent: A new approach to self-supervised Learning
2006.07733
https://arxiv.org/abs/2006.07733v3
https://arxiv.org/pdf/2006.07733v3.pdf
https://github.com/miszkur/SelfSupervisedLearning
false
false
true
tf
https://paperswithcode.com/paper/grad-cam-visual-explanations-from-deep
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
1610.02391
https://arxiv.org/abs/1610.02391v4
https://arxiv.org/pdf/1610.02391v4.pdf
https://github.com/karandesaiii/CheXNet
false
false
true
pytorch
https://paperswithcode.com/paper/chexnet-radiologist-level-pneumonia-detection
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
1711.05225
http://arxiv.org/abs/1711.05225v3
http://arxiv.org/pdf/1711.05225v3.pdf
https://github.com/karandesaiii/CheXNet
false
false
true
pytorch
https://paperswithcode.com/paper/learning-to-diagnose-from-scratch-by
Learning to diagnose from scratch by exploiting dependencies among labels
1710.10501
http://arxiv.org/abs/1710.10501v2
http://arxiv.org/pdf/1710.10501v2.pdf
https://github.com/karandesaiii/CheXNet
false
false
true
pytorch
https://paperswithcode.com/paper/chestx-ray8-hospital-scale-chest-x-ray
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
1705.02315
http://arxiv.org/abs/1705.02315v5
http://arxiv.org/pdf/1705.02315v5.pdf
https://github.com/karandesaiii/CheXNet
false
false
true
pytorch
https://paperswithcode.com/paper/the-r2d2-prior-for-generalized-linear-mixed
The R2D2 Prior for Generalized Linear Mixed Models
2111.10718
https://arxiv.org/abs/2111.10718v3
https://arxiv.org/pdf/2111.10718v3.pdf
https://github.com/eyanchenko/r2d2glmm
true
true
true
none
https://paperswithcode.com/paper/how-to-fine-tune-bert-for-text-classification
How to Fine-Tune BERT for Text Classification?
1905.05583
https://arxiv.org/abs/1905.05583v3
https://arxiv.org/pdf/1905.05583v3.pdf
https://github.com/Derposoft/ai-educator
false
false
true
none
https://paperswithcode.com/paper/the-network-structure-of-cultural-distances
Cultures as networks of cultural traits: A unifying framework for measuring culture and cultural distances
2007.02359
https://arxiv.org/abs/2007.02359v5
https://arxiv.org/pdf/2007.02359v5.pdf
https://github.com/rrondinelli/cultural-networks
true
true
false
none
https://paperswithcode.com/paper/deformable-detr-deformable-transformers-for-1
Deformable DETR: Deformable Transformers for End-to-End Object Detection
2010.04159
https://arxiv.org/abs/2010.04159v4
https://arxiv.org/pdf/2010.04159v4.pdf
https://github.com/Li-ai-cell/Interpretation_DETR
false
false
true
pytorch
https://paperswithcode.com/paper/end-to-end-object-detection-with-transformers
End-to-End Object Detection with Transformers
2005.12872
https://arxiv.org/abs/2005.12872v3
https://arxiv.org/pdf/2005.12872v3.pdf
https://github.com/Li-ai-cell/Interpretation_DETR
false
false
true
pytorch
https://paperswithcode.com/paper/maslow-s-hammer-for-catastrophic-forgetting
Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation
2205.09029
https://arxiv.org/abs/2205.09029v1
https://arxiv.org/pdf/2205.09029v1.pdf
https://github.com/seblee97/student_teacher_catastrophic
true
true
false
pytorch
https://paperswithcode.com/paper/auto-encoding-score-distribution-regression
Auto-Encoding Score Distribution Regression for Action Quality Assessment
2111.11029
https://arxiv.org/abs/2111.11029v2
https://arxiv.org/pdf/2111.11029v2.pdf
https://github.com/InfoX-SEU/DAE-AQA
true
true
true
pytorch
https://paperswithcode.com/paper/efficient-quantized-sparse-matrix-operations
Efficient Quantized Sparse Matrix Operations on Tensor Cores
2209.06979
https://arxiv.org/abs/2209.06979v4
https://arxiv.org/pdf/2209.06979v4.pdf
https://github.com/shigangli/magicube
true
true
false
pytorch
https://paperswithcode.com/paper/towards-better-stability-and-adaptability
Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation
null
http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_Towards_Better_Stability_and_Adaptability_Improve_Online_Self-Training_for_Model_CVPR_2023_paper.html
http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_Towards_Better_Stability_and_Adaptability_Improve_Online_Self-Training_for_Model_CVPR_2023_paper.pdf
https://github.com/dzhaoxd/dt-st
true
true
false
pytorch
https://paperswithcode.com/paper/random-quantum-neural-networks-rqnn-for-noisy
Random Quantum Neural Networks (RQNN) for Noisy Image Recognition
2203.01764
https://arxiv.org/abs/2203.01764v1
https://arxiv.org/pdf/2203.01764v1.pdf
https://github.com/darthsimpus/RQNN
true
true
false
pytorch
https://paperswithcode.com/paper/selecting-the-best-optimizing-system
Selecting the Best Optimizing System
2201.03065
https://arxiv.org/abs/2201.03065v1
https://arxiv.org/pdf/2201.03065v1.pdf
https://github.com/nian-si/selectoptsys
true
true
false
none
https://paperswithcode.com/paper/exploiting-adapters-for-cross-lingual-low
Exploiting Adapters for Cross-lingual Low-resource Speech Recognition
2105.11905
https://arxiv.org/abs/2105.11905v2
https://arxiv.org/pdf/2105.11905v2.pdf
https://github.com/jindongwang/transferlearning
true
true
true
pytorch
https://paperswithcode.com/paper/rate-coding-or-direct-coding-which-one-is
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?
2202.03133
https://arxiv.org/abs/2202.03133v2
https://arxiv.org/pdf/2202.03133v2.pdf
https://github.com/intelligent-computing-lab-yale/rate-vs-direct
true
true
true
pytorch
https://paperswithcode.com/paper/frozen-in-time-a-joint-video-and-image
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
2104.00650
https://arxiv.org/abs/2104.00650v2
https://arxiv.org/pdf/2104.00650v2.pdf
https://github.com/princetonvisualai/mqvr
false
false
true
pytorch
https://paperswithcode.com/paper/token-labeling-training-a-85-5-top-1-accuracy
All Tokens Matter: Token Labeling for Training Better Vision Transformers
2104.10858
https://arxiv.org/abs/2104.10858v3
https://arxiv.org/pdf/2104.10858v3.pdf
https://github.com/sail-sg/dualformer
false
false
true
pytorch
https://paperswithcode.com/paper/grad-cam-visual-explanations-from-deep
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
1610.02391
https://arxiv.org/abs/1610.02391v4
https://arxiv.org/pdf/1610.02391v4.pdf
https://github.com/sail-sg/dualformer
false
false
true
pytorch
https://paperswithcode.com/paper/multi-scale-one-class-recurrent-neural
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
2008.13361
https://arxiv.org/abs/2008.13361v1
https://arxiv.org/pdf/2008.13361v1.pdf
https://github.com/KnowledgeDiscovery/OC4Seq
true
false
false
pytorch
https://paperswithcode.com/paper/narwhal-and-tusk-a-dag-based-mempool-and
Narwhal and Tusk: A DAG-based Mempool and Efficient BFT Consensus
2105.11827
https://arxiv.org/abs/2105.11827v4
https://arxiv.org/pdf/2105.11827v4.pdf
https://github.com/asonnino/narwhal
true
true
true
none
https://paperswithcode.com/paper/multi-uav-path-planning-for-wireless-data
Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning
2010.12461
https://arxiv.org/abs/2010.12461v3
https://arxiv.org/pdf/2010.12461v3.pdf
https://github.com/hbayerlein/uav_data_harvesting
true
true
true
tf
https://paperswithcode.com/paper/multivariate-functional-group-sparse
Multivariate functional group sparse regression: functional predictor selection
2107.02146
https://arxiv.org/abs/2107.02146v2
https://arxiv.org/pdf/2107.02146v2.pdf
https://github.com/Ali-Mahzarnia/MFSGrp
true
true
true
none
https://paperswithcode.com/paper/graph-convolutional-modules-for-traffic
DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting
1905.12256
https://arxiv.org/abs/1905.12256v3
https://arxiv.org/pdf/1905.12256v3.pdf
https://github.com/snu-adsl/DDP-GCN
true
true
true
tf
https://paperswithcode.com/paper/inferring-dark-matter-substructure-with
Inferring dark matter substructure with astrometric lensing beyond the power spectrum
2110.01620
https://arxiv.org/abs/2110.01620v2
https://arxiv.org/pdf/2110.01620v2.pdf
https://github.com/smsharma/neural-global-astrometry
true
true
true
pytorch
https://paperswithcode.com/paper/3d-shape-reconstruction-from-free-hand
3D Shape Reconstruction from Free-Hand Sketches
2006.09694
https://arxiv.org/abs/2006.09694v2
https://arxiv.org/pdf/2006.09694v2.pdf
https://github.com/samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches
true
true
true
tf
https://paperswithcode.com/paper/learning-selection-masks-for-deep-neural
Input Selection for Bandwidth-Limited Neural Network Inference
1906.04673
https://arxiv.org/abs/1906.04673v2
https://arxiv.org/pdf/1906.04673v2.pdf
https://github.com/stefoe/selection-masks
true
true
true
pytorch
https://paperswithcode.com/paper/transmil-transformer-based-correlated
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
2106.00908
https://arxiv.org/abs/2106.00908v2
https://arxiv.org/pdf/2106.00908v2.pdf
https://github.com/Ycblue/TransMIL
false
false
true
pytorch
https://paperswithcode.com/paper/boosting-multiple-sclerosis-lesion
Boosting multiple sclerosis lesion segmentation through attention mechanism
2304.10790
https://arxiv.org/abs/2304.10790v1
https://arxiv.org/pdf/2304.10790v1.pdf
https://github.com/ictlab-unict/attention-cnn-MS-segmentation
true
false
false
pytorch
https://paperswithcode.com/paper/latent-autoregressive-source-separation
Latent Autoregressive Source Separation
2301.08562
https://arxiv.org/abs/2301.08562v1
https://arxiv.org/pdf/2301.08562v1.pdf
https://github.com/gladia-research-group/latent-autoregressive-source-separation
true
false
false
pytorch
https://paperswithcode.com/paper/on-approximate-data-reduction-for-the-rural
On approximate data reduction for the Rural Postman Problem: Theory and experiments
1812.10131
http://arxiv.org/abs/1812.10131v3
http://arxiv.org/pdf/1812.10131v3.pdf
https://gitlab.com/rvb/rpp-psaks
true
true
true
none
https://paperswithcode.com/paper/hvs-inspired-signal-degradation-network-for
HVS-Inspired Signal Degradation Network for Just Noticeable Difference Estimation
2208.07583
https://arxiv.org/abs/2208.07583v1
https://arxiv.org/pdf/2208.07583v1.pdf
https://github.com/jianjin008/hvs-sd-jnd
true
true
false
none
https://paperswithcode.com/paper/improving-unsupervised-defect-segmentation-by
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
1807.02011
http://arxiv.org/abs/1807.02011v3
http://arxiv.org/pdf/1807.02011v3.pdf
https://github.com/meitalB/NN
false
false
true
tf
https://paperswithcode.com/paper/semantic-communication-an-information
Semantic Information Recovery in Wireless Networks
2204.13366
https://arxiv.org/abs/2204.13366v4
https://arxiv.org/pdf/2204.13366v4.pdf
https://github.com/ant-uni-bremen/sinfony
true
false
false
tf
https://paperswithcode.com/paper/qagan-adversarial-approach-to-learning-domain
QAGAN: Adversarial Approach To Learning Domain Invariant Language Features
2206.12388
https://arxiv.org/abs/2206.12388v1
https://arxiv.org/pdf/2206.12388v1.pdf
https://github.com/towardsautonomy/qagan
true
true
false
pytorch
https://paperswithcode.com/paper/adarnn-adaptive-learning-and-forecasting-of
AdaRNN: Adaptive Learning and Forecasting of Time Series
2108.04443
https://arxiv.org/abs/2108.04443v2
https://arxiv.org/pdf/2108.04443v2.pdf
https://github.com/jindongwang/transferlearning
true
true
true
pytorch
https://paperswithcode.com/paper/kan-kolmogorov-arnold-networks
KAN: Kolmogorov-Arnold Networks
2404.19756
https://arxiv.org/abs/2404.19756v5
https://arxiv.org/pdf/2404.19756v5.pdf
https://github.com/Ipsedo/KolmogorovArnoldNetworks
false
false
false
pytorch
https://paperswithcode.com/paper/searching-for-activation-functions
Searching for Activation Functions
1710.05941
http://arxiv.org/abs/1710.05941v2
http://arxiv.org/pdf/1710.05941v2.pdf
https://github.com/ShubAn1901/License-Plate-Recognition
false
false
true
pytorch
https://paperswithcode.com/paper/temporal-robustness-of-stochastic-signals
Temporal Robustness of Stochastic Signals
2202.02583
https://arxiv.org/abs/2202.02583v2
https://arxiv.org/pdf/2202.02583v2.pdf
https://github.com/temporalrobrisk/temporal-robustness-risk
true
true
false
none
https://paperswithcode.com/paper/hypergraph-neural-networks-for-hypergraph
Hypergraph Neural Networks for Hypergraph Matching
null
http://openaccess.thecvf.com//content/ICCV2021/html/Liao_Hypergraph_Neural_Networks_for_Hypergraph_Matching_ICCV_2021_paper.html
http://openaccess.thecvf.com//content/ICCV2021/papers/Liao_Hypergraph_Neural_Networks_for_Hypergraph_Matching_ICCV_2021_paper.pdf
https://github.com/xwliao/hnn-hm
true
true
false
none
https://paperswithcode.com/paper/libact-pool-based-active-learning-in-python
libact: Pool-based Active Learning in Python
1710.00379
http://arxiv.org/abs/1710.00379v1
http://arxiv.org/pdf/1710.00379v1.pdf
https://github.com/melkherj/puddle
false
false
true
none
https://paperswithcode.com/paper/realistic-evaluation-of-deep-semi-supervised
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
1804.09170
https://arxiv.org/abs/1804.09170v4
https://arxiv.org/pdf/1804.09170v4.pdf
https://github.com/melkherj/puddle
false
false
true
none
https://paperswithcode.com/paper/deep-predictive-coding-networks-for-video
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
1605.08104
http://arxiv.org/abs/1605.08104v5
http://arxiv.org/pdf/1605.08104v5.pdf
https://github.com/nikolasmcneal/music-prediction
false
false
true
pytorch
https://paperswithcode.com/paper/curriculum-learning-with-infant-egocentric
Curriculum Learning With Infant Egocentric Videos
null
https://openreview.net/forum?id=zkfyOkBVpz
https://openreview.net/pdf?id=zkfyOkBVpz
https://github.com/ssheybani/baby-vision-curriculum
true
false
false
pytorch
https://paperswithcode.com/paper/channel-aware-contrastive-conditional
Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting
2410.02168
https://arxiv.org/abs/2410.02168v1
https://arxiv.org/pdf/2410.02168v1.pdf
https://github.com/LSY-Cython/CCDM
true
true
false
pytorch
https://paperswithcode.com/paper/a-dual-input-aware-factorization-machine-for
A Dual Input-aware Factorization Machine for CTR Prediction
null
https://www.ijcai.org/proceedings/2020/434
https://www.ijcai.org/proceedings/2020/0434.pdf
https://github.com/Andy1314Chen/DIFM-Paddle
false
false
false
paddle
https://paperswithcode.com/paper/xci-sketch-extraction-of-color-information
XCI-Sketch: Extraction of Color Information from Images for Generation of Colored Outlines and Sketches
2108.11554
https://arxiv.org/abs/2108.11554v2
https://arxiv.org/pdf/2108.11554v2.pdf
https://github.com/Sampai28/XCI-Sketch
false
false
true
none
https://paperswithcode.com/paper/constrained-policy-optimization
Constrained Policy Optimization
1705.10528
http://arxiv.org/abs/1705.10528v1
http://arxiv.org/pdf/1705.10528v1.pdf
https://github.com/Bigpig4396/PyTorch-Constrained-Policy-Optimization-CPO
false
false
true
pytorch
https://paperswithcode.com/paper/autolabel-clip-based-framework-for-open-set
AutoLabel: CLIP-based framework for Open-set Video Domain Adaptation
2304.01110
https://arxiv.org/abs/2304.01110v2
https://arxiv.org/pdf/2304.01110v2.pdf
https://github.com/gzaraunitn/autolabel
true
true
true
pytorch
https://paperswithcode.com/paper/respecting-time-series-properties-makes-deep
Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect
2207.10941
https://arxiv.org/abs/2207.10941v1
https://arxiv.org/pdf/2207.10941v1.pdf
https://github.com/origamisl/rtnet
true
true
true
pytorch
https://paperswithcode.com/paper/algorithms-and-radiation-dynamics-for-the
Algorithms and radiation dynamics for the vicinity of black holes I. Methods and codes
2212.01532
https://arxiv.org/abs/2212.01532v1
https://arxiv.org/pdf/2212.01532v1.pdf
https://gitlab.com/leelamichaels/Tranquillity
false
true
false
none
https://paperswithcode.com/paper/leaping-through-tree-space-continuous
Leaping through tree space: continuous phylogenetic inference for rooted and unrooted trees
2306.05739
https://arxiv.org/abs/2306.05739v4
https://arxiv.org/pdf/2306.05739v4.pdf
https://github.com/neclow/gradme
true
true
false
jax
https://paperswithcode.com/paper/biomimetic-tactile-receptors-for-3d-printed
Artificial SA-I and RA-I Afferents for Tactile Sensing of Ridges and Gratings
2107.02084
https://arxiv.org/abs/2107.02084v3
https://arxiv.org/pdf/2107.02084v3.pdf
https://github.com/nlepora/afferents-tactile-gratings-jrsi2022
true
true
false
none
https://paperswithcode.com/paper/exploring-the-state-of-the-art-language
Transformers on Multilingual Clause-Level Morphology
2211.01736
https://arxiv.org/abs/2211.01736v2
https://arxiv.org/pdf/2211.01736v2.pdf
https://github.com/emrecanacikgoz/mrl2022
true
true
false
pytorch
https://paperswithcode.com/paper/lovecon-text-driven-training-free-long-video
LOVECon: Text-driven Training-Free Long Video Editing with ControlNet
2310.09711
https://arxiv.org/abs/2310.09711v3
https://arxiv.org/pdf/2310.09711v3.pdf
https://github.com/zhijie-group/lovecon
true
true
false
pytorch
https://paperswithcode.com/paper/simulations-of-crystal-nucleation-from
Simulations of Crystal Nucleation from Solution at Constant Chemical Potential
1907.04037
https://arxiv.org/abs/1907.04037v1
https://arxiv.org/pdf/1907.04037v1.pdf
https://github.com/Tarakk/plumed-cumd
false
false
true
none
https://paperswithcode.com/paper/convex-clustering-through-mm-an-efficient
Convex Clustering through MM: An Efficient Algorithm to Perform Hierarchical Clustering
2211.01877
https://arxiv.org/abs/2211.01877v2
https://arxiv.org/pdf/2211.01877v2.pdf
https://github.com/djwtouw/ccmm-paper
true
true
false
none
https://paperswithcode.com/paper/argscichat-a-dataset-for-argumentative
ArgSciChat: A Dataset for Argumentative Dialogues on Scientific Papers
2202.06690
https://arxiv.org/abs/2202.06690v3
https://arxiv.org/pdf/2202.06690v3.pdf
https://github.com/federicoruggeri/argscichat_project
true
true
true
none
https://paperswithcode.com/paper/hawkeye-a-pytorch-based-library-for-fine
Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning
2310.09600
https://arxiv.org/abs/2310.09600v2
https://arxiv.org/pdf/2310.09600v2.pdf
https://github.com/hawkeye-finegrained/hawkeye
true
true
false
pytorch
https://paperswithcode.com/paper/twitter-dataset-for-2022-russo-ukrainian
Twitter Dataset for 2022 Russo-Ukrainian Crisis
2203.02955
https://arxiv.org/abs/2203.02955v1
https://arxiv.org/pdf/2203.02955v1.pdf
https://github.com/ehsanulhaq1/russo_ukraine_dataset
true
true
true
none
https://paperswithcode.com/paper/an-approach-for-generating-families-of
An Approach for Generating Families of Energetically Optimal Gaits from Passive Dynamic Walking Gaits
2303.14750
https://arxiv.org/abs/2303.14750v2
https://arxiv.org/pdf/2303.14750v2.pdf
https://github.com/nr-codes/optimalgaitsforcompassgait
true
true
false
none
https://paperswithcode.com/paper/how-much-does-attention-actually-attend
How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers
2211.03495
https://arxiv.org/abs/2211.03495v1
https://arxiv.org/pdf/2211.03495v1.pdf
https://github.com/schwartz-lab-nlp/papa
true
true
true
pytorch
https://paperswithcode.com/paper/on-first-order-meta-learning-algorithms
On First-Order Meta-Learning Algorithms
1803.02999
http://arxiv.org/abs/1803.02999v3
http://arxiv.org/pdf/1803.02999v3.pdf
https://github.com/Yuzhe-CHEN/NerfSNN
false
false
true
pytorch
https://paperswithcode.com/paper/semantics-adaptive-activation-intervention
Semantics-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors
2410.12299
https://arxiv.org/abs/2410.12299v1
https://arxiv.org/pdf/2410.12299v1.pdf
https://github.com/weixuan-wang123/SADI
true
true
true
pytorch
https://paperswithcode.com/paper/a-recommender-system-for-automatic-picking-of
A recommender system for automatic picking of subsurface formation tops
2202.08869
https://arxiv.org/abs/2202.08869v1
https://arxiv.org/pdf/2202.08869v1.pdf
https://github.com/jessepisel/matrixfactorization
true
true
false
none
https://paperswithcode.com/paper/disco-remedy-self-supervised-learning-on
DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning
2104.09124
https://arxiv.org/abs/2104.09124v7
https://arxiv.org/pdf/2104.09124v7.pdf
https://github.com/Yuting-Gao/DisCo-pytorch
true
true
true
pytorch
https://paperswithcode.com/paper/dual-path-cnn-with-max-gated-block-for-text
Dual-path CNN with Max Gated block for Text-Based Person Re-identification
2009.09343
https://arxiv.org/abs/2009.09343v1
https://arxiv.org/pdf/2009.09343v1.pdf
https://github.com/voriarty/Dual-path-CNN-with-Max-Gated-block-for-Text-Based-Person-Re-identification
true
false
false
pytorch
https://paperswithcode.com/paper/solving-satisfiability-modulo-counting-for
Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration With Provable Guarantees
2309.08883
https://arxiv.org/abs/2309.08883v2
https://arxiv.org/pdf/2309.08883v2.pdf
https://github.com/jil016/xor-smc
true
true
false
none
https://paperswithcode.com/paper/on-automatic-feasibility-study-for-machine
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise
2010.08410
https://arxiv.org/abs/2010.08410v4
https://arxiv.org/pdf/2010.08410v4.pdf
https://github.com/ds3lab/snoopy-paper
true
true
false
tf
https://paperswithcode.com/paper/residual-network-and-embedding-usage-new
Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks
2105.08330
https://arxiv.org/abs/2105.08330v2
https://arxiv.org/pdf/2105.08330v2.pdf
https://github.com/ytchx1999/GCN_res-CS-v2
false
false
true
pytorch
https://paperswithcode.com/paper/talking-heads-attention
Talking-Heads Attention
2003.02436
https://arxiv.org/abs/2003.02436v1
https://arxiv.org/pdf/2003.02436v1.pdf
https://github.com/zygmuntz/hyperband
true
false
false
none
https://paperswithcode.com/paper/interpretable-self-aware-neural-networks-for
Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction
2211.08701
https://arxiv.org/abs/2211.08701v1
https://arxiv.org/pdf/2211.08701v1.pdf
https://github.com/sisl/interpretableselfawareprediction
true
true
true
pytorch
https://paperswithcode.com/paper/bsn-boundary-sensitive-network-for-temporal
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
1806.02964
http://arxiv.org/abs/1806.02964v3
http://arxiv.org/pdf/1806.02964v3.pdf
https://github.com/wzmsltw/BSN-boundary-sensitive-network
false
true
true
pytorch
https://paperswithcode.com/paper/type-driven-multi-turn-corrections-for
Type-Driven Multi-Turn Corrections for Grammatical Error Correction
2203.09136
https://arxiv.org/abs/2203.09136v1
https://arxiv.org/pdf/2203.09136v1.pdf
https://github.com/deeplearnxmu/tmtc
true
true
false
pytorch
https://paperswithcode.com/paper/koopman-based-differentiable-predictive
Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem
2203.08984
https://arxiv.org/abs/2203.08984v1
https://arxiv.org/pdf/2203.08984v1.pdf
https://github.com/pnnl/neuromancer
true
true
false
pytorch
https://paperswithcode.com/paper/modeling-microlensing-events-with-mulensmodel
Modeling microlensing events with MulensModel
1803.01003
http://arxiv.org/abs/1803.01003v3
http://arxiv.org/pdf/1803.01003v3.pdf
https://github.com/rpoleski/MulensModel
true
true
true
none
https://paperswithcode.com/paper/edge-based-local-push-for-personalized
Edge-based Local Push for Personalized PageRank
2203.07937
https://arxiv.org/abs/2203.07937v2
https://arxiv.org/pdf/2203.07937v2.pdf
https://github.com/wanghzccls/edgepush
true
true
false
none
https://paperswithcode.com/paper/streaming-velocity-effects-on-the-post
Streaming Velocity Effects on the Post-reionization 21 cm Baryon Acoustic Oscillation Signal
2107.07615
https://arxiv.org/abs/2107.07615v2
https://arxiv.org/pdf/2107.07615v2.pdf
https://github.com/cosmosheep/hipowerspectrum
true
true
true
none
https://paperswithcode.com/paper/a-reduction-to-binary-approach-for-debiasing
A Reduction to Binary Approach for Debiasing Multiclass Datasets
2205.15860
https://arxiv.org/abs/2205.15860v2
https://arxiv.org/pdf/2205.15860v2.pdf
https://github.com/google-research/google-research/tree/master/ml_debiaser
true
false
false
jax
https://paperswithcode.com/paper/dverge-diversifying-vulnerabilities-for
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
2009.14720
https://arxiv.org/abs/2009.14720v2
https://arxiv.org/pdf/2009.14720v2.pdf
https://github.com/wang-axis/dna
false
false
true
pytorch
https://paperswithcode.com/paper/disrupting-adversarial-transferability-in
Disrupting Adversarial Transferability in Deep Neural Networks
2108.12492
https://arxiv.org/abs/2108.12492v3
https://arxiv.org/pdf/2108.12492v3.pdf
https://github.com/wang-axis/dna
true
true
false
pytorch
https://paperswithcode.com/paper/a-unified-transformer-framework-for-group
A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection
2203.04708
https://arxiv.org/abs/2203.04708v2
https://arxiv.org/pdf/2203.04708v2.pdf
https://github.com/suyukun666/UFO
true
true
true
pytorch
https://paperswithcode.com/paper/end-to-end-symbolic-regression-with
End-to-end symbolic regression with transformers
2204.10532
https://arxiv.org/abs/2204.10532v1
https://arxiv.org/pdf/2204.10532v1.pdf
https://github.com/facebookresearch/symbolicregression
true
true
true
pytorch
https://paperswithcode.com/paper/a-microlensing-search-of-700-million-vvv
A microlensing search of 700 million VVV light curves
2106.15617
https://arxiv.org/abs/2106.15617v1
https://arxiv.org/pdf/2106.15617v1.pdf
https://github.com/zofiakaczmarek/nested_ulens_parallax
false
false
true
none
https://paperswithcode.com/paper/dynesty-a-dynamic-nested-sampling-package-for
dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences
1904.02180
http://arxiv.org/abs/1904.02180v1
http://arxiv.org/pdf/1904.02180v1.pdf
https://github.com/zofiakaczmarek/nested_ulens_parallax
false
false
true
none
https://paperswithcode.com/paper/convert-an-application-to-faq-answering
ConveRT for FAQ Answering
2108.00719
https://arxiv.org/abs/2108.00719v3
https://arxiv.org/pdf/2108.00719v3.pdf
https://github.com/clips/ADATaLKS
false
false
true
none
https://paperswithcode.com/paper/an-accurate-unsupervised-method-for-joint
An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection
2203.05147
https://arxiv.org/abs/2203.05147v1
https://arxiv.org/pdf/2203.05147v1.pdf
https://github.com/luosx18/ued
true
true
false
pytorch
https://paperswithcode.com/paper/modeling-relational-data-with-graph
Modeling Relational Data with Graph Convolutional Networks
1703.06103
http://arxiv.org/abs/1703.06103v4
http://arxiv.org/pdf/1703.06103v4.pdf
https://github.com/shijx12/kqapro_baselines
false
false
true
pytorch
https://paperswithcode.com/paper/bart-denoising-sequence-to-sequence-pre
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
1910.13461
https://arxiv.org/abs/1910.13461v1
https://arxiv.org/pdf/1910.13461v1.pdf
https://github.com/shijx12/kqapro_baselines
false
false
true
pytorch
https://paperswithcode.com/paper/cross-modal-map-learning-for-vision-and
Cross-modal Map Learning for Vision and Language Navigation
2203.05137
https://arxiv.org/abs/2203.05137v3
https://arxiv.org/pdf/2203.05137v3.pdf
https://github.com/ggeorgak11/cm2
true
true
false
pytorch
https://paperswithcode.com/paper/large-scale-gan-training-for-high-fidelity
Large Scale GAN Training for High Fidelity Natural Image Synthesis
1809.11096
http://arxiv.org/abs/1809.11096v2
http://arxiv.org/pdf/1809.11096v2.pdf
https://github.com/abhi8585/GeneratedART-NFT-VISION
false
false
true
tf
https://paperswithcode.com/paper/self-attention-generative-adversarial
Self-Attention Generative Adversarial Networks
1805.08318
https://arxiv.org/abs/1805.08318v2
https://arxiv.org/pdf/1805.08318v2.pdf
https://github.com/abhi8585/GeneratedART-NFT-VISION
false
false
true
tf