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

Dataset card Data Studio Files
xet
Community
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https://paperswithcode.com/paper/language-models-are-few-shot-learners
Language Models are Few-Shot Learners
2005.14165
https://arxiv.org/abs/2005.14165v4
https://arxiv.org/pdf/2005.14165v4.pdf
https://github.com/bigscience-workshop/Megatron-DeepSpeed
false
false
true
pytorch
https://paperswithcode.com/paper/stable-architectures-for-deep-neural-networks
Stable Architectures for Deep Neural Networks
1705.03341
http://arxiv.org/abs/1705.03341v3
http://arxiv.org/pdf/1705.03341v3.pdf
https://github.com/DecodEPFL/HamiltonianNet
false
false
true
pytorch
https://paperswithcode.com/paper/a-unified-framework-for-hamiltonian-deep
A unified framework for Hamiltonian deep neural networks
2104.13166
https://arxiv.org/abs/2104.13166v1
https://arxiv.org/pdf/2104.13166v1.pdf
https://github.com/DecodEPFL/HamiltonianNet
false
false
true
pytorch
https://paperswithcode.com/paper/hamiltonian-deep-neural-networks-guaranteeing
Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design
2105.13205
https://arxiv.org/abs/2105.13205v2
https://arxiv.org/pdf/2105.13205v2.pdf
https://github.com/DecodEPFL/HamiltonianNet
true
true
true
pytorch
https://paperswithcode.com/paper/fictitious-gan-training-gans-with-historical
Fictitious GAN: Training GANs with Historical Models
1803.08647
http://arxiv.org/abs/1803.08647v2
http://arxiv.org/pdf/1803.08647v2.pdf
https://github.com/pijel/fGAN
false
false
true
pytorch
https://paperswithcode.com/paper/reciprocal-transformations-for-unsupervised
Reciprocal Transformations for Unsupervised Video Object Segmentation
null
http://openaccess.thecvf.com//content/CVPR2021/html/Ren_Reciprocal_Transformations_for_Unsupervised_Video_Object_Segmentation_CVPR_2021_paper.html
http://openaccess.thecvf.com//content/CVPR2021/papers/Ren_Reciprocal_Transformations_for_Unsupervised_Video_Object_Segmentation_CVPR_2021_paper.pdf
https://github.com/OliverRensu/RTNet
true
true
false
pytorch
https://paperswithcode.com/paper/tensor-networks-contraction-and-the-belief
Tensor Networks contraction and the Belief Propagation algorithm
2008.04433
http://arxiv.org/abs/2008.04433v2
http://arxiv.org/pdf/2008.04433v2.pdf
https://github.com/RoyElkabetz/Belief-propagation_Tensor-Networks
false
false
true
none
https://paperswithcode.com/paper/training-deep-learning-based-denoisers
Training Deep Learning Based Denoisers without Ground Truth Data
1803.01314
https://arxiv.org/abs/1803.01314v4
https://arxiv.org/pdf/1803.01314v4.pdf
https://github.com/rshnn/xray-denoising
false
false
true
tf
https://paperswithcode.com/paper/large-capacity-and-flexible-video
Large-capacity and Flexible Video Steganography via Invertible Neural Network
2304.12300
https://arxiv.org/abs/2304.12300v1
https://arxiv.org/pdf/2304.12300v1.pdf
https://github.com/mc-e/lf-vsn
true
true
true
pytorch
https://paperswithcode.com/paper/world-model-as-a-graph-learning-latent
World Model as a Graph: Learning Latent Landmarks for Planning
2011.12491
https://arxiv.org/abs/2011.12491v3
https://arxiv.org/pdf/2011.12491v3.pdf
https://github.com/LunjunZhang/world-model-as-a-graph
true
true
true
pytorch
https://paperswithcode.com/paper/deeptime-a-python-library-for-machine
Deeptime: a Python library for machine learning dynamical models from time series data
2110.15013
https://arxiv.org/abs/2110.15013v2
https://arxiv.org/pdf/2110.15013v2.pdf
https://github.com/deeptime-ml/deeptime
true
true
false
none
https://paperswithcode.com/paper/agent-workflow-memory
Agent Workflow Memory
2409.07429
https://arxiv.org/abs/2409.07429v1
https://arxiv.org/pdf/2409.07429v1.pdf
https://github.com/zorazrw/agent-workflow-memory
true
true
true
none
https://paperswithcode.com/paper/training-generative-adversarial-networks-in
Training Generative Adversarial Networks in One Stage
2103.00430
https://arxiv.org/abs/2103.00430v3
https://arxiv.org/pdf/2103.00430v3.pdf
https://github.com/zju-vipa/OSGAN
true
true
true
pytorch
https://paperswithcode.com/paper/a-topic-coverage-approach-to-evaluation-of
A Topic Coverage Approach to Evaluation of Topic Models
2012.06274
https://arxiv.org/abs/2012.06274v3
https://arxiv.org/pdf/2012.06274v3.pdf
https://github.com/dkorenci/topic_coverage
true
true
false
none
https://paperswithcode.com/paper/detecting-message-modification-attacks-on-the
Detecting message modification attacks on the CAN bus with Temporal Convolutional Networks
2106.08692
https://arxiv.org/abs/2106.08692v1
https://arxiv.org/pdf/2106.08692v1.pdf
https://github.com/CrySyS/can-log-infector
true
true
false
none
https://paperswithcode.com/paper/diagnostic-tests-for-nested-sampling
Diagnostic Tests for Nested Sampling Calculations
1804.06406
http://arxiv.org/abs/1804.06406v1
http://arxiv.org/pdf/1804.06406v1.pdf
https://github.com/ejhigson/perfectns
false
false
true
none
https://paperswithcode.com/paper/deeptag-a-general-framework-for-fiducial
DeepTag: A General Framework for Fiducial Marker Design and Detection
2105.13731
https://arxiv.org/abs/2105.13731v2
https://arxiv.org/pdf/2105.13731v2.pdf
https://github.com/herohuyongtao/deeptag-pytorch
false
false
true
pytorch
https://paperswithcode.com/paper/bootstrap-based-inference-for-cube-root
Bootstrap-Based Inference for Cube Root Asymptotics
1704.08066
http://arxiv.org/abs/1704.08066v3
http://arxiv.org/pdf/1704.08066v3.pdf
https://github.com/mdcattaneo/replication-CJN_2020_ECMA
true
false
false
none
https://paperswithcode.com/paper/layer-folding-neural-network-depth-reduction
Layer Folding: Neural Network Depth Reduction using Activation Linearization
2106.09309
https://arxiv.org/abs/2106.09309v2
https://arxiv.org/pdf/2106.09309v2.pdf
https://github.com/LayerFolding/Layer-Folding
true
true
true
pytorch
https://paperswithcode.com/paper/spreadgnn-serverless-multi-task-federated
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
2106.02743
https://arxiv.org/abs/2106.02743v1
https://arxiv.org/pdf/2106.02743v1.pdf
https://github.com/FedML-AI/SpreadGNN
true
false
false
pytorch
https://paperswithcode.com/paper/ne2001-i-a-new-model-for-the-galactic
NE2001.I. A New Model for the Galactic Distribution of Free Electrons and its Fluctuations
astro-ph/0207156
https://arxiv.org/abs/astro-ph/0207156v3
https://arxiv.org/pdf/astro-ph/0207156v3.pdf
https://github.com/v-morello/pyne2001
false
false
true
none
https://paperswithcode.com/paper/learn-to-use-future-information-in
Temporally Correlated Task Scheduling for Sequence Learning
2007.05290
https://arxiv.org/abs/2007.05290v2
https://arxiv.org/pdf/2007.05290v2.pdf
https://github.com/microsoft/qlib/tree/main/examples/benchmarks/TCTS
true
false
false
pytorch
https://paperswithcode.com/paper/many-shot-from-low-shot-learning-to-annotate
Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection
2008.09694
https://arxiv.org/abs/2008.09694v2
https://arxiv.org/pdf/2008.09694v2.pdf
https://github.com/dyabel/wsod-mmdet
false
false
true
pytorch
https://paperswithcode.com/paper/group-fairness-in-bandit-arm-selection
Group Fairness in Bandit Arm Selection
1912.03802
https://arxiv.org/abs/1912.03802v3
https://arxiv.org/pdf/1912.03802v3.pdf
https://github.com/candiceschumann/groupfairtreatment
true
true
false
none
https://paperswithcode.com/paper/generalized-end-to-end-loss-for-speaker
Generalized End-to-End Loss for Speaker Verification
1710.10467
https://arxiv.org/abs/1710.10467v5
https://arxiv.org/pdf/1710.10467v5.pdf
https://github.com/dalonlobo/diarization-experiments
false
false
true
tf
https://paperswithcode.com/paper/pyro-nn-python-reconstruction-operators-in
PYRO-NN: Python Reconstruction Operators in Neural Networks
1904.13342
http://arxiv.org/abs/1904.13342v1
http://arxiv.org/pdf/1904.13342v1.pdf
https://github.com/csyben/PYRO-NN-Layers
true
true
true
tf
https://paperswithcode.com/paper/ultrasound-video-transformers-for-cardiac
Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation
2107.00977
https://arxiv.org/abs/2107.00977v1
https://arxiv.org/pdf/2107.00977v1.pdf
https://github.com/HReynaud/UVT
true
true
true
pytorch
https://paperswithcode.com/paper/adaptive-client-sampling-in-federated
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
2112.14332
https://arxiv.org/abs/2112.14332v5
https://arxiv.org/pdf/2112.14332v5.pdf
https://github.com/boxinz17/fl-client-sampling
true
true
false
pytorch
https://paperswithcode.com/paper/contextual-importance-and-utility
Contextual Importance and Utility: aTheoretical Foundation
2202.07292
https://arxiv.org/abs/2202.07292v1
https://arxiv.org/pdf/2202.07292v1.pdf
https://github.com/karyframling/ajcai_2021
true
true
false
none
https://paperswithcode.com/paper/midibert-piano-large-scale-pre-training-for
BERT-like Pre-training for Symbolic Piano Music Classification Tasks
2107.05223
https://arxiv.org/abs/2107.05223v2
https://arxiv.org/pdf/2107.05223v2.pdf
https://github.com/wazenmai/MIDI-BERT
true
true
true
pytorch
https://paperswithcode.com/paper/ava-avd-audio-visual-speaker-diarization-in
AVA-AVD: Audio-Visual Speaker Diarization in the Wild
2111.14448
https://arxiv.org/abs/2111.14448v5
https://arxiv.org/pdf/2111.14448v5.pdf
https://github.com/zcxu-eric/ava-avd
true
true
true
pytorch
https://paperswithcode.com/paper/stochastic-parametrization-using-compressed
Stochastic Parameterization using Compressed Sensing: Application to the Lorenz-96 Atmospheric Model
2106.14110
https://arxiv.org/abs/2106.14110v2
https://arxiv.org/pdf/2106.14110v2.pdf
https://github.com/amartyamukherjee/StochasticParametrization-ApplicationToLorenz96
true
false
false
none
https://paperswithcode.com/paper/deciphering-bitcoin-blockchain-data-by-cohort
Deciphering Bitcoin Blockchain Data by Cohort Analysis
2103.00173
https://arxiv.org/abs/2103.00173v3
https://arxiv.org/pdf/2103.00173v3.pdf
https://github.com/SciEcon/UTXO
true
true
true
none
https://paperswithcode.com/paper/balanced-allocation-in-batches-the-tower-of
Balanced Allocations in Batches: The Tower of Two Choices
2302.04399
https://arxiv.org/abs/2302.04399v2
https://arxiv.org/pdf/2302.04399v2.pdf
https://github.com/Dim131/Batched-23
true
false
true
none
https://paperswithcode.com/paper/deepdiva-a-highly-functional-python-framework
DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments
1805.00329
http://arxiv.org/abs/1805.00329v1
http://arxiv.org/pdf/1805.00329v1.pdf
https://github.com/ashlaban/ltu-adl-2019
false
false
true
pytorch
https://paperswithcode.com/paper/a-contrastive-divergence-for-combining
A Contrastive Divergence for Combining Variational Inference and MCMC
1905.04062
https://arxiv.org/abs/1905.04062v2
https://arxiv.org/pdf/1905.04062v2.pdf
https://github.com/suryabulusu/VCD
false
false
true
none
https://paperswithcode.com/paper/laplace-matching-for-fast-approximate
Laplace Matching for fast Approximate Inference in Latent Gaussian Models
2105.03109
https://arxiv.org/abs/2105.03109v2
https://arxiv.org/pdf/2105.03109v2.pdf
https://github.com/mariushobbhahn/Laplace_Matching_for_GLMs
true
true
true
pytorch
https://paperswithcode.com/paper/dynamic-cat-swarm-optimization-algorithm-for
Dynamic Cat Swarm Optimization Algorithm for Backboard Wiring Problem
2107.08908
https://arxiv.org/abs/2107.08908v1
https://arxiv.org/pdf/2107.08908v1.pdf
https://github.com/aramahmed/DCSO-Algorithm
true
true
false
none
https://paperswithcode.com/paper/computational-benefits-of-intermediate
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning
2107.03961
https://arxiv.org/abs/2107.03961v5
https://arxiv.org/pdf/2107.03961v5.pdf
https://github.com/kebaek/minigrid
true
true
true
pytorch
https://paperswithcode.com/paper/blocking-techniques-for-sparse-matrix
Blocking Techniques for Sparse Matrix Multiplication on Tensor Accelerators
2202.05868
https://arxiv.org/abs/2202.05868v1
https://arxiv.org/pdf/2202.05868v1.pdf
https://github.com/lacsfub/sparta
true
true
false
none
https://paperswithcode.com/paper/deep-subspace-clustering-networks
Deep Subspace Clustering Networks
1709.02508
http://arxiv.org/abs/1709.02508v1
http://arxiv.org/pdf/1709.02508v1.pdf
https://github.com/adidenkov/Deep-Subspace-Clustering
false
false
true
tf
https://paperswithcode.com/paper/dcn-m-improved-deep-cross-network-for-feature
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
2008.13535
https://arxiv.org/abs/2008.13535v2
https://arxiv.org/pdf/2008.13535v2.pdf
https://github.com/LinJayan/DCN_V2_Paddle
false
false
false
paddle
https://paperswithcode.com/paper/highly-accurate-protein-structure-prediction
Highly accurate protein structure prediction with AlphaFold
null
https://www.nature.com/articles/s41586-021-03819-2
https://www.nature.com/articles/s41586-021-03819-2_reference.pdf
https://github.com/deepmind/alphafold
true
true
false
jax
https://paperswithcode.com/paper/tromr-transformer-based-polyphonic-optical
TrOMR:Transformer-Based Polyphonic Optical Music Recognition
2308.09370
https://arxiv.org/abs/2308.09370v1
https://arxiv.org/pdf/2308.09370v1.pdf
https://github.com/netease/polyphonic-tromr
true
true
false
pytorch
https://paperswithcode.com/paper/a-deep-learning-approach-to-probabilistic
A Deep Learning Approach to Probabilistic Forecasting of Weather
2203.12529
https://arxiv.org/abs/2203.12529v2
https://arxiv.org/pdf/2203.12529v2.pdf
https://github.com/rittlern/probabilistic_forecasting
true
true
false
tf
https://paperswithcode.com/paper/gram-generalization-in-deep-rl-with-a-robust
GRAM: Generalization in Deep RL with a Robust Adaptation Module
2412.04323
https://arxiv.org/abs/2412.04323v2
https://arxiv.org/pdf/2412.04323v2.pdf
https://github.com/merlresearch/gram
true
true
true
pytorch
https://paperswithcode.com/paper/high-dimensional-sparse-bayesian-learning
High-Dimensional Sparse Bayesian Learning without Covariance Matrices
2202.12808
https://arxiv.org/abs/2202.12808v1
https://arxiv.org/pdf/2202.12808v1.pdf
https://github.com/al5250/sparse-bayes-learn
true
true
false
pytorch
https://paperswithcode.com/paper/debiased-sinkhorn-barycenters
Debiased Sinkhorn barycenters
2006.02575
https://arxiv.org/abs/2006.02575v1
https://arxiv.org/pdf/2006.02575v1.pdf
https://github.com/ott-jax/ott
false
false
true
jax
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/OlgaChernytska/Super-Resolution-with-FSRCNN
false
false
true
tf
https://paperswithcode.com/paper/locating-and-editing-factual-knowledge-in-gpt
Locating and Editing Factual Associations in GPT
2202.05262
https://arxiv.org/abs/2202.05262v5
https://arxiv.org/pdf/2202.05262v5.pdf
https://github.com/kmeng01/rome
true
false
false
pytorch
https://paperswithcode.com/paper/inference-of-signaling-mechanism-from
Detection of signaling mechanisms from cellular responses to multiple cues
2205.02699
https://arxiv.org/abs/2205.02699v2
https://arxiv.org/pdf/2205.02699v2.pdf
https://github.com/souticksaha21/inference-of-signaling-mechanism-from-cellular-responses-to-multiple-cues-version-2
true
true
false
none
https://paperswithcode.com/paper/learning-to-identify-perceptual-bugs-in-3d
Learning to Identify Perceptual Bugs in 3D Video Games
2202.12884
https://arxiv.org/abs/2202.12884v1
https://arxiv.org/pdf/2202.12884v1.pdf
https://github.com/benedictwilkins/world-of-bugs-experiments
true
true
true
pytorch
https://paperswithcode.com/paper/testing-deep-neural-network-based-image
Testing DNN Image Classifiers for Confusion & Bias Errors
1905.07831
https://arxiv.org/abs/1905.07831v3
https://arxiv.org/pdf/1905.07831v3.pdf
https://github.com/ARiSE-Lab/DeepInspect
true
true
true
pytorch
https://paperswithcode.com/paper/a-simple-model-for-subject-behavior-in
A Simple Model for Subject Behavior in Subjective Experiments
2004.02067
https://arxiv.org/abs/2004.02067v3
https://arxiv.org/pdf/2004.02067v3.pdf
https://github.com/Netflix/sureal
true
true
false
none
https://paperswithcode.com/paper/referencing-sources-of-molecular
Referencing Sources of Molecular Spectroscopic Data in the Era of Data Science: Application to the HITRAN and AMBDAS Databases
2005.07544
http://arxiv.org/abs/2005.07544v1
http://arxiv.org/pdf/2005.07544v1.pdf
https://github.com/hitranonline/refs
true
true
false
none
https://paperswithcode.com/paper/semantics-stgcnn-a-semantics-guided-spatial
Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction
2108.04740
https://arxiv.org/abs/2108.04740v1
https://arxiv.org/pdf/2108.04740v1.pdf
https://github.com/yutasq/multi-class-social-stgcnn
true
true
false
pytorch
https://paperswithcode.com/paper/infrared-dust-echoes-from-neutron-star
Infrared dust echoes from neutron star mergers
2108.04243
https://arxiv.org/abs/2108.04243v1
https://arxiv.org/pdf/2108.04243v1.pdf
https://github.com/wenbinlu/dustecho
true
true
false
none
https://paperswithcode.com/paper/soft-sensing-transformer-hundreds-of-sensors
Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word
2111.05973
https://arxiv.org/abs/2111.05973v1
https://arxiv.org/pdf/2111.05973v1.pdf
https://github.com/seagate/softsensingtransformer
true
true
false
none
https://paperswithcode.com/paper/optimal-transport-tools-ott-a-jax-toolbox-for
Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein
2201.12324
https://arxiv.org/abs/2201.12324v1
https://arxiv.org/pdf/2201.12324v1.pdf
https://github.com/ott-jax/ott
true
true
false
jax
https://paperswithcode.com/paper/data-clustering-and-noise-undressing-for
Data clustering and noise undressing for correlation matrices
cond-mat/0101237
https://arxiv.org/abs/cond-mat/0101237v1
https://arxiv.org/pdf/cond-mat/0101237v1.pdf
https://github.com/tehraio/timeseries_gen
false
false
true
none
https://paperswithcode.com/paper/variational-dropout-via-empirical-bayes
Variational Dropout via Empirical Bayes
1811.00596
http://arxiv.org/abs/1811.00596v2
http://arxiv.org/pdf/1811.00596v2.pdf
https://github.com/ivannz/cplxmodule
false
false
true
pytorch
https://paperswithcode.com/paper/sp-gan-sphere-guided-3d-shape-generation-and
SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation
2108.04476
https://arxiv.org/abs/2108.04476v1
https://arxiv.org/pdf/2108.04476v1.pdf
https://github.com/liruihui/sp-gan
true
true
false
pytorch
https://paperswithcode.com/paper/classifier-calibration-with-implications-to
Classifier Calibration: with application to threat scores in cybersecurity
2102.05143
https://arxiv.org/abs/2102.05143v3
https://arxiv.org/pdf/2102.05143v3.pdf
https://github.com/isotlaboratory/ClassifierCalibration-Code
true
true
true
none
https://paperswithcode.com/paper/sanity-checks-for-saliency-maps
Sanity Checks for Saliency Maps
1810.03292
https://arxiv.org/abs/1810.03292v3
https://arxiv.org/pdf/1810.03292v3.pdf
https://github.com/pytorch/captum
false
false
true
pytorch
https://paperswithcode.com/paper/textual-inference-getting-logic-from-humans
Textual Inference: getting logic from humans
null
https://aclanthology.org/W17-6915
https://aclanthology.org/W17-6915.pdf
https://github.com/kkalouli/SICK-processing
true
true
false
none
https://paperswithcode.com/paper/striving-for-simplicity-the-all-convolutional
Striving for Simplicity: The All Convolutional Net
1412.6806
http://arxiv.org/abs/1412.6806v3
http://arxiv.org/pdf/1412.6806v3.pdf
https://github.com/pytorch/captum
false
false
true
pytorch
https://paperswithcode.com/paper/harp-hierarchical-representation-learning-for
HARP: Hierarchical Representation Learning for Networks
1706.07845
http://arxiv.org/abs/1706.07845v2
http://arxiv.org/pdf/1706.07845v2.pdf
https://github.com/GTmac/HARP
false
false
true
none
https://paperswithcode.com/paper/context-selection-for-embedding-models
Context Selection for Embedding Models
null
http://papers.nips.cc/paper/7067-context-selection-for-embedding-models
http://papers.nips.cc/paper/7067-context-selection-for-embedding-models.pdf
https://github.com/blei-lab/context-selection-embedding
true
true
false
tf
https://paperswithcode.com/paper/learning-fair-rule-lists
Learning Fair Rule Lists
1909.03977
https://arxiv.org/abs/1909.03977v2
https://arxiv.org/pdf/1909.03977v2.pdf
https://github.com/ferryjul/fairCORELS
false
false
true
none
https://paperswithcode.com/paper/a-unified-approach-to-interpreting-model
A Unified Approach to Interpreting Model Predictions
1705.07874
http://arxiv.org/abs/1705.07874v2
http://arxiv.org/pdf/1705.07874v2.pdf
https://github.com/pytorch/captum
false
false
false
pytorch
https://paperswithcode.com/paper/packaging-research-artefacts-with-ro-crate
Packaging research artefacts with RO-Crate
2108.06503
https://arxiv.org/abs/2108.06503v2
https://arxiv.org/pdf/2108.06503v2.pdf
https://github.com/stain/ro-crate-paper
true
true
false
none
https://paperswithcode.com/paper/neuracrypt-is-not-private
NeuraCrypt is not private
2108.07256
https://arxiv.org/abs/2108.07256v1
https://arxiv.org/pdf/2108.07256v1.pdf
https://github.com/yala/NeuraCrypt-Challenge
true
true
false
none
https://paperswithcode.com/paper/pressure-induced-shape-shifting-of-helical
Pressure-induced Shape-shifting of Helical Bacteria
2205.09688
https://arxiv.org/abs/2205.09688v2
https://arxiv.org/pdf/2205.09688v2.pdf
https://github.com/gerland-group/reinforced_tube_helix
true
true
false
none
https://paperswithcode.com/paper/deep-mri-reconstruction-with-radial
Deep MRI Reconstruction with Radial Subsampling
2108.07619
https://arxiv.org/abs/2108.07619v3
https://arxiv.org/pdf/2108.07619v3.pdf
https://github.com/directgroup/direct
true
true
false
pytorch
https://paperswithcode.com/paper/integrating-object-aware-and-interaction
Integrating Object-aware and Interaction-aware Knowledge for Weakly Supervised Scene Graph Generation
2208.01834
https://arxiv.org/abs/2208.01834v1
https://arxiv.org/pdf/2208.01834v1.pdf
https://github.com/xcppy/ws-sgg
true
true
false
pytorch
https://paperswithcode.com/paper/deep-learning-based-quantum-vortex-detection
Deep learning based quantum vortex detection in atomic Bose-Einstein condensates
2012.13097
https://arxiv.org/abs/2012.13097v2
https://arxiv.org/pdf/2012.13097v2.pdf
https://github.com/frmetz/quantum_vortex_detection
true
true
true
none
https://paperswithcode.com/paper/a-comparison-of-centrality-measures-for-graph
A Comparison of Centrality Measures for Graph-Based Keyphrase Extraction
null
https://aclanthology.org/I13-1102
https://aclanthology.org/I13-1102.pdf
https://github.com/boudinfl/centrality_measures_ijcnlp13
true
true
false
none
https://paperswithcode.com/paper/exact-3d-scattering-solutions-for-spherical
Exact 3D scattering solutions for spherical symmetric scatterers
2204.09581
https://arxiv.org/abs/2204.09581v1
https://arxiv.org/pdf/2204.09581v1.pdf
https://github.com/zetison/e3dss
true
true
false
none
https://paperswithcode.com/paper/bossnas-exploring-hybrid-cnn-transformers
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search
2103.12424
https://arxiv.org/abs/2103.12424v3
https://arxiv.org/pdf/2103.12424v3.pdf
https://github.com/changlin31/BossNAS
true
true
true
pytorch
https://paperswithcode.com/paper/qibo-a-framework-for-quantum-simulation-with
Qibo: a framework for quantum simulation with hardware acceleration
2009.01845
https://arxiv.org/abs/2009.01845v2
https://arxiv.org/pdf/2009.01845v2.pdf
https://github.com/qiboteam/qibojit
false
false
true
none
https://paperswithcode.com/paper/manifold-constrained-nucleus-level-denoising
Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design
2409.10584
https://arxiv.org/abs/2409.10584v2
https://arxiv.org/pdf/2409.10584v2.pdf
https://github.com/yanliang3612/nucleusdiff
true
true
true
pytorch
https://paperswithcode.com/paper/low-frequency-tilt-seismology-with-a
Low Frequency Tilt Seismology with a Precision Ground Rotation Sensor
1707.03084
http://arxiv.org/abs/1707.03084v3
http://arxiv.org/pdf/1707.03084v3.pdf
https://github.com/mpross/Single-Station-Seismology
false
false
true
none
https://paperswithcode.com/paper/canet-a-context-aware-network-for-shadow
CANet: A Context-Aware Network for Shadow Removal
2108.09894
https://arxiv.org/abs/2108.09894v1
https://arxiv.org/pdf/2108.09894v1.pdf
https://github.com/zipei-chen/canet
true
true
false
pytorch
https://paperswithcode.com/paper/verbcl-a-dataset-of-verbatim-quotes-for
VerbCL: A Dataset of Verbatim Quotes for Highlight Extraction in Case Law
2108.10120
https://arxiv.org/abs/2108.10120v1
https://arxiv.org/pdf/2108.10120v1.pdf
https://github.com/j-rossi-nl/verbcl
true
true
false
none
https://paperswithcode.com/paper/new-q-newton-s-method-meets-backtracking-line
New Q-Newton's method meets Backtracking line search: good convergence guarantee, saddle points avoidance, quadratic rate of convergence, and easy implementation
2108.10249
https://arxiv.org/abs/2108.10249v1
https://arxiv.org/pdf/2108.10249v1.pdf
https://github.com/tuyenttMathOslo/New-Q-Newton-s-method-Backtracking
true
false
false
none
https://paperswithcode.com/paper/bugs4q-a-benchmark-of-real-bugs-for-quantum
Bugs4Q: A Benchmark of Real Bugs for Quantum Programs
2108.09744
https://arxiv.org/abs/2108.09744v2
https://arxiv.org/pdf/2108.09744v2.pdf
https://github.com/z-928/bugs4q
true
true
false
none
https://paperswithcode.com/paper/openmatch-open-set-consistency-regularization
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers
2105.14148
https://arxiv.org/abs/2105.14148v2
https://arxiv.org/pdf/2105.14148v2.pdf
https://github.com/VisionLearningGroup/OP_Match
true
true
true
pytorch
https://paperswithcode.com/paper/an-end-to-end-differentiable-framework-for
An End-to-End Differentiable Framework for Contact-Aware Robot Design
2107.07501
https://arxiv.org/abs/2107.07501v2
https://arxiv.org/pdf/2107.07501v2.pdf
https://github.com/eanswer/DiffHand
true
false
false
none
https://paperswithcode.com/paper/advancing-self-supervised-monocular-depth
Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR
2109.09628
https://arxiv.org/abs/2109.09628v4
https://arxiv.org/pdf/2109.09628v4.pdf
https://github.com/fengziyue/FusionDepth
true
false
true
pytorch
https://paperswithcode.com/paper/byzantine-robust-variance-reduced-federated
Byzantine-Robust Variance-Reduced Federated Learning over Distributed Non-i.i.d. Data
2009.08161
https://arxiv.org/abs/2009.08161v2
https://arxiv.org/pdf/2009.08161v2.pdf
https://github.com/pengj97/byzantine-robust-variance-reduction
true
true
true
none
https://paperswithcode.com/paper/master-memory-function-for-delay-based
Master memory function for delay-based reservoir computers with single-variable dynamics
2108.12643
https://arxiv.org/abs/2108.12643v1
https://arxiv.org/pdf/2108.12643v1.pdf
https://github.com/Rincewind1989/MMF
true
false
true
none
https://paperswithcode.com/paper/an-introduction-to-variational-autoencoders
An Introduction to Variational Autoencoders
1906.02691
https://arxiv.org/abs/1906.02691v3
https://arxiv.org/pdf/1906.02691v3.pdf
https://github.com/GiuliaLavizzari/ML4thesis
false
false
true
tf
https://paperswithcode.com/paper/variational-autoencoders-for-new-physics
Variational Autoencoders for New Physics Mining at the Large Hadron Collider
1811.10276
https://arxiv.org/abs/1811.10276v3
https://arxiv.org/pdf/1811.10276v3.pdf
https://github.com/GiuliaLavizzari/ML4thesis
false
false
true
tf
https://paperswithcode.com/paper/measuring-complexity-of-learning-schemes
Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation
2112.06209
https://arxiv.org/abs/2112.06209v2
https://arxiv.org/pdf/2112.06209v2.pdf
https://github.com/joaquimcampos/htv-learn
true
true
false
pytorch
https://paperswithcode.com/paper/simple-pose-rethinking-and-improving-a-bottom
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
1911.10529
https://arxiv.org/abs/1911.10529v1
https://arxiv.org/pdf/1911.10529v1.pdf
https://github.com/Mind23-2/MindCode-88/tree/main/simple_pose
false
false
false
mindspore
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/raj-gupta1/Flower-Species-Classification
false
false
true
tf
https://paperswithcode.com/paper/who-should-i-engage-with-at-what-time-a
Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network
2301.08399
https://arxiv.org/abs/2301.08399v1
https://arxiv.org/pdf/2301.08399v1.pdf
https://github.com/hit-ices/tnnls-mtgn
true
true
false
pytorch
https://paperswithcode.com/paper/recurrent-gaussian-processes
Recurrent Gaussian Processes
1511.06644
http://arxiv.org/abs/1511.06644v6
http://arxiv.org/pdf/1511.06644v6.pdf
https://github.com/zhenwendai/RGP
true
false
false
none
https://paperswithcode.com/paper/learning-recommendations-from-user-actions-in
Learning Recommendations from User Actions in the Item-poor Insurance Domain
2211.15360
https://arxiv.org/abs/2211.15360v1
https://arxiv.org/pdf/2211.15360v1.pdf
https://github.com/simonebbruun/cross-sessions_rs
true
true
false
tf
https://paperswithcode.com/paper/transformer-networks-for-data-augmentation-of
Transformer Networks for Data Augmentation of Human Physical Activity Recognition
2109.01081
https://arxiv.org/abs/2109.01081v2
https://arxiv.org/pdf/2109.01081v2.pdf
https://github.com/sandeep-189/data-augmentation
true
true
false
pytorch