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

Dataset card Data Studio Files
xet
Community
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https://paperswithcode.com/paper/ranking-aggregation-with-interactive-feedback
Ranking Aggregation with Interactive Feedback for Collaborative Person Re-identification
null
https://bmvc2022.mpi-inf.mpg.de/386/
https://bmvc2022.mpi-inf.mpg.de/0386.pdf
https://github.com/2023-MindSpore-1/ms-code-137
false
false
false
mindspore
https://paperswithcode.com/paper/on-recognizing-texts-of-arbitrary-shapes-with
On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention
1910.04396
https://arxiv.org/abs/1910.04396v1
https://arxiv.org/pdf/1910.04396v1.pdf
https://github.com/Media-Smart/vedastr
false
false
true
pytorch
https://paperswithcode.com/paper/on-the-robustness-of-interpretability-methods
On the Robustness of Interpretability Methods
1806.08049
http://arxiv.org/abs/1806.08049v1
http://arxiv.org/pdf/1806.08049v1.pdf
https://github.com/pytorch/captum
false
false
false
pytorch
https://paperswithcode.com/paper/efficient-robust-optimal-transport
Efficient Robust Optimal Transport with Application to Multi-Label Classification
2010.11852
https://arxiv.org/abs/2010.11852v2
https://arxiv.org/pdf/2010.11852v2.pdf
https://github.com/SatyadevNtv/ROT4C
true
true
true
tf
https://paperswithcode.com/paper/comprehensive-view-of-microscopic
Comprehensive view of microscopic interactions between DNA-coated colloids
2111.06468
https://arxiv.org/abs/2111.06468v1
https://arxiv.org/pdf/2111.06468v1.pdf
https://github.com/smarbach/dnacoatedcolloidsinteractions
true
true
false
none
https://paperswithcode.com/paper/interpretation-of-neural-networks-is-fragile
Interpretation of Neural Networks is Fragile
1710.10547
http://arxiv.org/abs/1710.10547v2
http://arxiv.org/pdf/1710.10547v2.pdf
https://github.com/pytorch/captum
false
false
false
pytorch
https://paperswithcode.com/paper/deep-residual-inception-encoder-decoder-1
Deep residual inception encoder-decoder network for amyloid PET harmonization
null
https://alz-journals.onlinelibrary.wiley.com/doi/full/10.1002/alz.12564
https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz.12564
https://github.com/jaygshah/RIED-Net
false
false
false
pytorch
https://paperswithcode.com/paper/adposition-and-case-supersenses-v2-guidelines
Adposition and Case Supersenses v2.6: Guidelines for English
1704.02134
https://arxiv.org/abs/1704.02134v8
https://arxiv.org/pdf/1704.02134v8.pdf
https://github.com/nert-gu/streusle
true
true
true
none
https://paperswithcode.com/paper/bisect-learning-to-split-and-rephrase
BiSECT: Learning to Split and Rephrase Sentences with Bitexts
2109.05006
https://arxiv.org/abs/2109.05006v1
https://arxiv.org/pdf/2109.05006v1.pdf
https://github.com/mounicam/bisect
true
true
true
none
https://paperswithcode.com/paper/stiff-neural-ordinary-differential-equations
Stiff Neural Ordinary Differential Equations
2103.15341
https://arxiv.org/abs/2103.15341v3
https://arxiv.org/pdf/2103.15341v3.pdf
https://github.com/DENG-MIT/StiffNeuralODE
true
true
true
none
https://paperswithcode.com/paper/dimension-reduction-of-dynamical-systems-on
Dimension reduction of dynamical systems on networks with leading and non-leading eigenvectors of adjacency matrices
2203.13872
https://arxiv.org/abs/2203.13872v2
https://arxiv.org/pdf/2203.13872v2.pdf
https://github.com/naokimas/nonleading-spectral
true
true
false
none
https://paperswithcode.com/paper/the-built-in-selection-bias-of-hazard-ratios
The built-in selection bias of hazard ratios formalized
2210.16550
https://arxiv.org/abs/2210.16550v1
https://arxiv.org/pdf/2210.16550v1.pdf
https://github.com/rajp93/chr
true
true
false
none
https://paperswithcode.com/paper/informer-beyond-efficient-transformer-for
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
2012.07436
https://arxiv.org/abs/2012.07436v3
https://arxiv.org/pdf/2012.07436v3.pdf
https://github.com/larsbentsen/fftransformer
false
false
true
pytorch
https://paperswithcode.com/paper/detecting-out-of-distribution-examples-with
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices
1912.12510
https://arxiv.org/abs/1912.12510v2
https://arxiv.org/pdf/1912.12510v2.pdf
https://github.com/kobybibas/pnml_ood_detection
false
false
false
pytorch
https://paperswithcode.com/paper/enhancing-the-reliability-of-out-of
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
1706.02690
https://arxiv.org/abs/1706.02690v5
https://arxiv.org/pdf/1706.02690v5.pdf
https://github.com/kobybibas/pnml_ood_detection
false
false
false
pytorch
https://paperswithcode.com/paper/a-baseline-for-detecting-misclassified-and
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
1610.02136
http://arxiv.org/abs/1610.02136v3
http://arxiv.org/pdf/1610.02136v3.pdf
https://github.com/kobybibas/pnml_ood_detection
false
false
false
pytorch
https://paperswithcode.com/paper/parlai-a-dialog-research-software-platform
ParlAI: A Dialog Research Software Platform
1705.06476
http://arxiv.org/abs/1705.06476v4
http://arxiv.org/pdf/1705.06476v4.pdf
https://github.com/min942773/parlai_wandb
false
false
true
pytorch
https://paperswithcode.com/paper/cxr-clip-toward-large-scale-chest-x-ray
CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training
2310.13292
https://arxiv.org/abs/2310.13292v1
https://arxiv.org/pdf/2310.13292v1.pdf
https://github.com/kakaobrain/cxr-clip
false
false
true
pytorch
https://paperswithcode.com/paper/rethinking-the-ill-posedness-of-the-spectral
Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help
2201.02564
https://arxiv.org/abs/2201.02564v2
https://arxiv.org/pdf/2201.02564v2.pdf
https://github.com/shuzheshi/spectralfunction
true
true
false
pytorch
https://paperswithcode.com/paper/towards-consensus-reducing-polarization-by
Towards Consensus: Reducing Polarization by Perturbing Social Networks
2206.08996
https://arxiv.org/abs/2206.08996v2
https://arxiv.org/pdf/2206.08996v2.pdf
https://github.com/drigobon/minimizing-polarization
true
true
false
none
https://paperswithcode.com/paper/efficiently-predicting-high-resolution-mass
Efficiently predicting high resolution mass spectra with graph neural networks
2301.11419
https://arxiv.org/abs/2301.11419v1
https://arxiv.org/pdf/2301.11419v1.pdf
https://github.com/samgoldman97/ms-pred
false
false
true
pytorch
https://paperswithcode.com/paper/generation-of-microbial-colonies-dataset-with
Generation of microbial colonies dataset with deep learning style transfer
2111.03789
https://arxiv.org/abs/2111.03789v2
https://arxiv.org/pdf/2111.03789v2.pdf
https://github.com/jarek-pawlowski/microbial-dataset-generation
true
true
false
pytorch
https://paperswithcode.com/paper/a-principle-solution-for-enroll-test-mismatch
A Principle Solution for Enroll-Test Mismatch in Speaker Recognition
2012.12471
https://arxiv.org/abs/2012.12471v2
https://arxiv.org/pdf/2012.12471v2.pdf
https://gitlab.com/csltstu/enroll-test-mismatch
true
true
false
pytorch
https://paperswithcode.com/paper/data-augmentation-through-monte-carlo
Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics
2109.09649
https://arxiv.org/abs/2109.09649v1
https://arxiv.org/pdf/2109.09649v1.pdf
https://github.com/gkpapers/2021aggregatemca
true
true
false
none
https://paperswithcode.com/paper/automatic-lane-change-scenario-extraction-and
Automatic lane change scenario extraction and generation of scenarios in OpenX format from real-world data
2203.07521
https://arxiv.org/abs/2203.07521v1
https://arxiv.org/pdf/2203.07521v1.pdf
https://github.com/dkarunakaran/scenario_extraction_framework
true
true
false
none
https://paperswithcode.com/paper/magnitude-corrected-and-time-aligned
Magnitude-Corrected and Time-Aligned Interpolation of Head-Related Transfer Functions
2303.09966
https://arxiv.org/abs/2303.09966v1
https://arxiv.org/pdf/2303.09966v1.pdf
https://github.com/audiogroupcologne/supdeq
true
true
false
none
https://paperswithcode.com/paper/building-a-heterogeneous-large-scale
A Sparse and Locally Coherent Morphable Face Model for Dense Semantic Correspondence Across Heterogeneous 3D Faces
2006.03840
https://arxiv.org/abs/2006.03840v3
https://arxiv.org/pdf/2006.03840v3.pdf
https://github.com/clferrari/SLC-3DMM
true
true
true
none
https://paperswithcode.com/paper/workspace-aware-online-grasp-planning
Workspace Aware Online Grasp Planning
1806.11402
http://arxiv.org/abs/1806.11402v1
http://arxiv.org/pdf/1806.11402v1.pdf
https://github.com/scikit-fmm/scikit-fmm
true
true
true
none
https://paperswithcode.com/paper/challenges-in-representation-learning-a
Challenges in Representation Learning: A report on three machine learning contests
1307.0414
http://arxiv.org/abs/1307.0414v1
http://arxiv.org/pdf/1307.0414v1.pdf
https://github.com/justinshenk/fer
false
false
true
tf
https://paperswithcode.com/paper/pc2-pu-patch-correlation-and-position
PC$^2$-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling
2109.09337
https://arxiv.org/abs/2109.09337v3
https://arxiv.org/pdf/2109.09337v3.pdf
https://github.com/chenlongwhu/pc2-pu
true
true
true
pytorch
https://paperswithcode.com/paper/roughness-index-and-roughness-distance-for
Roughness Index and Roughness Distance for Benchmarking Medical Segmentation
2103.12350
https://arxiv.org/abs/2103.12350v1
https://arxiv.org/pdf/2103.12350v1.pdf
https://github.com/Vidhiwar/roughness-index
false
false
true
pytorch
https://paperswithcode.com/paper/beyond-part-models-person-retrieval-with
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
1711.09349
http://arxiv.org/abs/1711.09349v3
http://arxiv.org/pdf/1711.09349v3.pdf
https://github.com/MindSpore-paper-code-2/code2/tree/main/pcb_rpp
false
false
false
mindspore
https://paperswithcode.com/paper/the-second-conversational-intelligence
The Second Conversational Intelligence Challenge (ConvAI2)
1902.00098
http://arxiv.org/abs/1902.00098v1
http://arxiv.org/pdf/1902.00098v1.pdf
https://github.com/af1tang/personaGPT
false
false
true
pytorch
https://paperswithcode.com/paper/towards-robust-monocular-depth-estimation
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
1907.01341
https://arxiv.org/abs/1907.01341v3
https://arxiv.org/pdf/1907.01341v3.pdf
https://github.com/mindspore-ai/models/tree/master/research/cv/midas
false
false
false
mindspore
https://paperswithcode.com/paper/the-pragmatics-behind-politics-modelling
The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse
null
https://aclanthology.org/2020.findings-emnlp.402
https://aclanthology.org/2020.findings-emnlp.402.pdf
https://github.com/littlepea13/mtl_political_discourse
true
true
false
pytorch
https://paperswithcode.com/paper/insight-into-cloud-processes-from
Insight into cloud processes from unsupervised classification with a rotationally invariant autoencoder
2211.00860
https://arxiv.org/abs/2211.00860v2
https://arxiv.org/pdf/2211.00860v2.pdf
https://github.com/rdcep/clouds
true
true
false
tf
https://paperswithcode.com/paper/when-is-wall-a-pared-and-when-a-muro
When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selection
2109.06014
https://arxiv.org/abs/2109.06014v1
https://arxiv.org/pdf/2109.06014v1.pdf
https://github.com/Aditi138/LexSelection
false
false
true
pytorch
https://paperswithcode.com/paper/stc-antispoofing-systems-for-the-asvspoof2019
STC Antispoofing Systems for the ASVspoof2019 Challenge
1904.05576
http://arxiv.org/abs/1904.05576v1
http://arxiv.org/pdf/1904.05576v1.pdf
https://github.com/ozora-ogino/LCNN
false
false
true
tf
https://paperswithcode.com/paper/repvgg-making-vgg-style-convnets-great-again
RepVGG: Making VGG-style ConvNets Great Again
2101.03697
https://arxiv.org/abs/2101.03697v3
https://arxiv.org/pdf/2101.03697v3.pdf
https://github.com/MindSpore-paper-code-2/code2/tree/main/repvgg
false
false
false
mindspore
https://paperswithcode.com/paper/noisy-networks-for-exploration
Noisy Networks for Exploration
1706.10295
https://arxiv.org/abs/1706.10295v3
https://arxiv.org/pdf/1706.10295v3.pdf
https://github.com/MOVzeroOne/DQN
false
false
true
pytorch
https://paperswithcode.com/paper/vortex-clustering-polarisation-and
Vortex clustering, polarisation and circulation intermittency in classical and quantum turbulence
2107.03335
https://arxiv.org/abs/2107.03335v2
https://arxiv.org/pdf/2107.03335v2.pdf
https://github.com/jipolanco/Circulation.jl
true
true
true
none
https://paperswithcode.com/paper/a-linear-adjustment-based-approach-to
A linear adjustment based approach to posterior drift in transfer learning
2111.10841
https://arxiv.org/abs/2111.10841v2
https://arxiv.org/pdf/2111.10841v2.pdf
https://github.com/smaityumich/linearly-shifted-transfer
true
true
false
none
https://paperswithcode.com/paper/towards-prediction-explainability-through
The Explanation Game: Towards Prediction Explainability through Sparse Communication
2004.13876
https://arxiv.org/abs/2004.13876v2
https://arxiv.org/pdf/2004.13876v2.pdf
https://github.com/deep-spin/spec
true
false
false
pytorch
https://paperswithcode.com/paper/playing-atari-with-deep-reinforcement
Playing Atari with Deep Reinforcement Learning
1312.5602
http://arxiv.org/abs/1312.5602v1
http://arxiv.org/pdf/1312.5602v1.pdf
https://github.com/MOVzeroOne/DQN
false
false
true
pytorch
https://paperswithcode.com/paper/dueling-network-architectures-for-deep
Dueling Network Architectures for Deep Reinforcement Learning
1511.06581
http://arxiv.org/abs/1511.06581v3
http://arxiv.org/pdf/1511.06581v3.pdf
https://github.com/MOVzeroOne/DQN
false
false
true
pytorch
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/MOVzeroOne/DQN
false
false
true
pytorch
https://paperswithcode.com/paper/gaze-estimation-with-an-ensemble-of-four
Gaze Estimation with an Ensemble of Four Architectures
2107.01980
https://arxiv.org/abs/2107.01980v1
https://arxiv.org/pdf/2107.01980v1.pdf
https://github.com/VIPL-TAL-GAZE/GAZE2021
false
false
true
pytorch
https://paperswithcode.com/paper/a-coherent-unsupervised-model-for-toponym
A Coherent Unsupervised Model for Toponym Resolution
1805.01952
https://arxiv.org/abs/1805.01952v2
https://arxiv.org/pdf/1805.01952v2.pdf
https://github.com/ehsk/CHF-TopoResolver
true
true
false
none
https://paperswithcode.com/paper/meta-learning-on-a-sequence-of-imbalanced
Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness
2109.14120
https://arxiv.org/abs/2109.14120v1
https://arxiv.org/pdf/2109.14120v1.pdf
https://github.com/joey-wang123/imbalancemeta
true
true
false
pytorch
https://paperswithcode.com/paper/transfer-learning-improving-neural-network
Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data
2105.05075
https://arxiv.org/abs/2105.05075v1
https://arxiv.org/pdf/2105.05075v1.pdf
https://github.com/djozinovi/TLpredIM
true
true
true
none
https://paperswithcode.com/paper/snips-voice-platform-an-embedded-spoken
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
1805.10190
http://arxiv.org/abs/1805.10190v3
http://arxiv.org/pdf/1805.10190v3.pdf
https://github.com/Priyanshiguptaaa/Intent_Recognition_BERT
false
false
true
tf
https://paperswithcode.com/paper/advhat-real-world-adversarial-attack-on
AdvHat: Real-world adversarial attack on ArcFace Face ID system
1908.08705
https://arxiv.org/abs/1908.08705v1
https://arxiv.org/pdf/1908.08705v1.pdf
https://github.com/MindSpore-paper-code-2/code400/tree/main/Arcface
false
false
false
mindspore
https://paperswithcode.com/paper/optimizing-memory-efficiency-of-graph
Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms
2104.03058
https://arxiv.org/abs/2104.03058v2
https://arxiv.org/pdf/2104.03058v2.pdf
https://github.com/BUAA-CI-Lab/GNN-Feature-Decomposition
true
true
true
pytorch
https://paperswithcode.com/paper/predictive-world-models-from-real-world
Predictive World Models from Real-World Partial Observations
2301.04783
https://arxiv.org/abs/2301.04783v3
https://arxiv.org/pdf/2301.04783v3.pdf
https://github.com/robin-karlsson0/predictive-world-models
true
true
true
pytorch
https://paperswithcode.com/paper/effectiveness-of-optimization-algorithms-in
Effectiveness of Optimization Algorithms in Deep Image Classification
2110.01598
https://arxiv.org/abs/2110.01598v1
https://arxiv.org/pdf/2110.01598v1.pdf
https://github.com/chuiyunjun/projectCSC413
true
true
false
none
https://paperswithcode.com/paper/ranking-policy-decisions
Ranking Policy Decisions
2008.13607
https://arxiv.org/abs/2008.13607v3
https://arxiv.org/pdf/2008.13607v3.pdf
https://github.com/anonuser-532438/policyrankinganon
true
true
true
pytorch
https://paperswithcode.com/paper/190501999
A Benchmark API Call Dataset for Windows PE Malware Classification
1905.01999
http://arxiv.org/abs/1905.01999v1
http://arxiv.org/pdf/1905.01999v1.pdf
https://github.com/6700github/awesome-reverse-engineering
false
false
true
paddle
https://paperswithcode.com/paper/deep-adaptive-input-normalization-for-price
Deep Adaptive Input Normalization for Time Series Forecasting
1902.07892
https://arxiv.org/abs/1902.07892v2
https://arxiv.org/pdf/1902.07892v2.pdf
https://github.com/vladserkoff/DAIN-pytorch
false
false
true
pytorch
https://paperswithcode.com/paper/dpc-unsupervised-deep-point-correspondence
DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction
2110.08636
https://arxiv.org/abs/2110.08636v1
https://arxiv.org/pdf/2110.08636v1.pdf
https://github.com/dvirginz/dpc
true
true
true
pytorch
https://paperswithcode.com/paper/lossless-compression-with-probabilistic-1
Lossless Compression with Probabilistic Circuits
2111.11632
https://arxiv.org/abs/2111.11632v2
https://arxiv.org/pdf/2111.11632v2.pdf
https://github.com/juice-jl/pressedjuice.jl
true
true
false
none
https://paperswithcode.com/paper/putting-nerf-on-a-diet-semantically
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis
2104.00677
https://arxiv.org/abs/2104.00677v1
https://arxiv.org/pdf/2104.00677v1.pdf
https://github.com/ajayjain/DietNeRF
true
false
true
pytorch
https://paperswithcode.com/paper/reduce-reformulation-of-mixed-integer
ReDUCE: Reformulation of Mixed Integer Programs using Data from Unsupervised Clusters for Learning Efficient Strategies
2110.00666
https://arxiv.org/abs/2110.00666v1
https://arxiv.org/pdf/2110.00666v1.pdf
https://github.com/romelaucla/reduce
true
true
true
none
https://paperswithcode.com/paper/step-by-step-a-hierarchical-framework-for
Step by step: a hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning
null
https://doi.org/10.1016/j.knosys.2022.108843
https://doi.org/10.1016/j.knosys.2022.108843
https://github.com/2023-MindSpore-1/ms-code-4
false
false
false
mindspore
https://paperswithcode.com/paper/memebusters-at-semeval-2020-task-8-feature
Memebusters at SemEval-2020 Task 8: Feature Fusion Model for Sentiment Analysis on Memes Using Transfer Learning
null
https://aclanthology.org/2020.semeval-1.154
https://aclanthology.org/2020.semeval-1.154.pdf
https://github.com/04mayukh/memebusters-at-semeval-2020-task-8-memotion-analysis
false
true
false
none
https://paperswithcode.com/paper/v2e-from-video-frames-to-realistic-dvs-event
v2e: From Video Frames to Realistic DVS Events
2006.07722
https://arxiv.org/abs/2006.07722v2
https://arxiv.org/pdf/2006.07722v2.pdf
https://github.com/SensorsINI/v2e_exps_public
false
false
true
pytorch
https://paperswithcode.com/paper/suod-toward-scalable-unsupervised-outlier
SUOD: Toward Scalable Unsupervised Outlier Detection
2002.03222
https://arxiv.org/abs/2002.03222v1
https://arxiv.org/pdf/2002.03222v1.pdf
https://github.com/yzhao062/SUOD
true
true
false
none
https://paperswithcode.com/paper/tackling-multi-answer-open-domain-questions
Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework
2110.08544
https://arxiv.org/abs/2110.08544v2
https://arxiv.org/pdf/2110.08544v2.pdf
https://github.com/zhihongshao/rectify
true
true
true
none
https://paperswithcode.com/paper/optimizing-readability-using-genetic
Optimizing Readability Using Genetic Algorithms
2301.00374
https://arxiv.org/abs/2301.00374v1
https://arxiv.org/pdf/2301.00374v1.pdf
https://github.com/jorge-martinez-gil/oruga
true
true
true
none
https://paperswithcode.com/paper/regularizing-variational-autoencoder-with
Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness
2110.12381
https://arxiv.org/abs/2110.12381v1
https://arxiv.org/pdf/2110.12381v1.pdf
https://github.com/smilesdzgk/du-vae
true
true
false
pytorch
https://paperswithcode.com/paper/conjugate-priors-for-count-and-rounded-data
Semiparametric discrete data regression with Monte Carlo inference and prediction
2110.12316
https://arxiv.org/abs/2110.12316v6
https://arxiv.org/pdf/2110.12316v6.pdf
https://github.com/drkowal/rSTAR
true
true
false
none
https://paperswithcode.com/paper/probabilistic-mixture-of-experts-for-1
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning
2104.09122
https://arxiv.org/abs/2104.09122v1
https://arxiv.org/pdf/2104.09122v1.pdf
https://github.com/JieRen98/rlkit-pmoe
true
false
true
pytorch
https://paperswithcode.com/paper/uncertainty-quantification-and-deep-ensembles
Uncertainty Quantification and Deep Ensembles
2007.08792
https://arxiv.org/abs/2007.08792v4
https://arxiv.org/pdf/2007.08792v4.pdf
https://github.com/rahulrahaman/Uncertainty-Quantification-and-Deep-Ensemble
true
true
true
pytorch
https://paperswithcode.com/paper/locally-differentially-private-contextual
Locally Differentially Private (Contextual) Bandits Learning
2006.00701
https://arxiv.org/abs/2006.00701v4
https://arxiv.org/pdf/2006.00701v4.pdf
https://github.com/mindspore-ai/models/tree/master/research/rl/ldp_linucb
false
false
false
mindspore
https://paperswithcode.com/paper/sample-size-estimation-using-a-latent
Sample Size Estimation using a Latent Variable Model for Mixed Outcome Co-Primary, Multiple Primary and Composite Endpoints
1912.05258
https://arxiv.org/abs/1912.05258v1
https://arxiv.org/pdf/1912.05258v1.pdf
https://github.com/martinamcm/mult_sampsize
false
false
true
none
https://paperswithcode.com/paper/layout-and-task-aware-instruction-prompt-for
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering
2306.00526
https://arxiv.org/abs/2306.00526v4
https://arxiv.org/pdf/2306.00526v4.pdf
https://github.com/deepopinion/anls_star_metric
false
false
true
none
https://paperswithcode.com/paper/deeper-depth-prediction-with-fully
Deeper Depth Prediction with Fully Convolutional Residual Networks
1606.00373
http://arxiv.org/abs/1606.00373v2
http://arxiv.org/pdf/1606.00373v2.pdf
https://github.com/danielzgsilva/MonoDepthAttacks
false
false
true
pytorch
https://paperswithcode.com/paper/lindblad-tomography-of-a-superconducting
Lindblad Tomography of a Superconducting Quantum Processor
2105.02338
https://arxiv.org/abs/2105.02338v5
https://arxiv.org/pdf/2105.02338v5.pdf
https://github.com/jborregaard/Lindblad_tomography
true
true
false
none
https://paperswithcode.com/paper/new-roads-to-the-small-scale-universe
New Roads to the Small-Scale Universe: Measurements of the Clustering of Matter with the High-Redshift UV Galaxy Luminosity Function
2110.13161
https://arxiv.org/abs/2110.13161v2
https://arxiv.org/pdf/2110.13161v2.pdf
https://github.com/nnssa/gallumi_public
true
true
true
none
https://paperswithcode.com/paper/the-jsonlite-package-a-practical-and
The jsonlite Package: A Practical and Consistent Mapping Between JSON Data and R Objects
1403.2805
http://arxiv.org/abs/1403.2805v1
http://arxiv.org/pdf/1403.2805v1.pdf
https://github.com/behrica/opencpu-clj
false
false
true
none
https://paperswithcode.com/paper/robust-control-of-partially-specified-boolean
Robust Control of Partially Specified Boolean Networks
2202.13440
https://arxiv.org/abs/2202.13440v1
https://arxiv.org/pdf/2202.13440v1.pdf
https://github.com/sybila/biodivine-pbn-control
true
true
false
none
https://paperswithcode.com/paper/gallumi-a-galaxy-luminosity-function-pipeline
GALLUMI: A Galaxy Luminosity Function Pipeline for Cosmology and Astrophysics
2110.13168
https://arxiv.org/abs/2110.13168v3
https://arxiv.org/pdf/2110.13168v3.pdf
https://github.com/nnssa/gallumi_public
true
true
true
none
https://paperswithcode.com/paper/time-series-graphical-lasso-and-sparse-var
Time Series Graphical Lasso and Sparse VAR Estimation
2107.01659
https://arxiv.org/abs/2107.01659v1
https://arxiv.org/pdf/2107.01659v1.pdf
https://github.com/adallak/TSGlasso/blob/main/README.md
false
false
false
none
https://paperswithcode.com/paper/wiener-filtering-and-pure-e-b-decomposition
Wiener filtering and pure E/B decomposition of CMB maps with anisotropic correlated noise
1906.10704
http://arxiv.org/abs/1906.10704v2
http://arxiv.org/pdf/1906.10704v2.pdf
https://github.com/doogesh/dante
false
false
true
none
https://paperswithcode.com/paper/post-hoc-domain-adaptation-via-guided-data
Post-Hoc Domain Adaptation via Guided Data Homogenization
2104.03624
https://arxiv.org/abs/2104.03624v1
https://arxiv.org/pdf/2104.03624v1.pdf
https://github.com/willisk/GDH
false
false
true
pytorch
https://paperswithcode.com/paper/mosaicking-to-distill-knowledge-distillation
Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data
2110.15094
https://arxiv.org/abs/2110.15094v1
https://arxiv.org/pdf/2110.15094v1.pdf
https://github.com/zju-vipa/mosaickd
true
true
true
pytorch
https://paperswithcode.com/paper/lf-yolo-a-lighter-and-faster-yolo-for-weld
LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-ray Image
2110.15045
https://arxiv.org/abs/2110.15045v2
https://arxiv.org/pdf/2110.15045v2.pdf
https://github.com/lmomoy/lf-yolo
true
true
true
pytorch
https://paperswithcode.com/paper/data-free-network-quantization-with
Data-Free Network Quantization With Adversarial Knowledge Distillation
2005.04136
https://arxiv.org/abs/2005.04136v1
https://arxiv.org/pdf/2005.04136v1.pdf
https://github.com/zju-vipa/mosaickd
false
false
true
pytorch
https://paperswithcode.com/paper/learning-from-a-teacher-using-unlabeled-data
Learning from a Teacher using Unlabeled Data
1911.05275
https://arxiv.org/abs/1911.05275v1
https://arxiv.org/pdf/1911.05275v1.pdf
https://github.com/zju-vipa/mosaickd
false
false
true
pytorch
https://paperswithcode.com/paper/cross-domain-object-detection-by-target
Cross Domain Object Detection by Target-Perceived Dual Branch Distillation
2205.01291
https://arxiv.org/abs/2205.01291v1
https://arxiv.org/pdf/2205.01291v1.pdf
https://github.com/feobi1999/tdd
true
true
true
pytorch
https://paperswithcode.com/paper/opt-open-pre-trained-transformer-language
OPT: Open Pre-trained Transformer Language Models
2205.01068
https://arxiv.org/abs/2205.01068v4
https://arxiv.org/pdf/2205.01068v4.pdf
https://github.com/MindCode-4/code-2/tree/main/opt
false
false
false
mindspore
https://paperswithcode.com/paper/adapttext-a-novel-framework-for-domain
AdaptText: A Novel Framework for Domain-Independent Automated Sinhala Text Classification
null
https://ieeexplore.ieee.org/document/9605861
https://ieeexplore.ieee.org/document/9605861
https://github.com/yathindrakodithuwakku/AdaptText
false
false
false
none
https://paperswithcode.com/paper/towards-unifying-feature-attribution-and
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End
2011.04917
https://arxiv.org/abs/2011.04917v3
https://arxiv.org/pdf/2011.04917v3.pdf
https://github.com/interpretml/DiCE
false
false
true
tf
https://paperswithcode.com/paper/web-based-elicitation-of-human-perception-on
Human-in-the-Loop Mixup
2211.01202
https://arxiv.org/abs/2211.01202v3
https://arxiv.org/pdf/2211.01202v3.pdf
https://github.com/cambridge-mlg/hill-mixup
true
true
false
none
https://paperswithcode.com/paper/augmenting-english-adjective-senses-with
Augmenting English Adjective Senses with Supersenses
null
https://aclanthology.org/L14-1073
https://aclanthology.org/L14-1073.pdf
https://github.com/ytsvetko/adjective_supersense_classifier
true
true
false
none
https://paperswithcode.com/paper/ugc-vqa-benchmarking-blind-video-quality
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
2005.14354
https://arxiv.org/abs/2005.14354v2
https://arxiv.org/pdf/2005.14354v2.pdf
https://github.com/tu184044109/VIDEVAL_release
true
true
true
none
https://paperswithcode.com/paper/last-iterate-convergence-of-optimistic
Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities
2205.08446
https://arxiv.org/abs/2205.08446v2
https://arxiv.org/pdf/2205.08446v2.pdf
https://github.com/eduardgorbunov/potentials_and_last_iter_convergence_for_vips
true
true
false
none
https://paperswithcode.com/paper/random-search-and-reproducibility-for-neural
Random Search and Reproducibility for Neural Architecture Search
1902.07638
https://arxiv.org/abs/1902.07638v3
https://arxiv.org/pdf/1902.07638v3.pdf
https://github.com/microsoft/nn-meter
false
false
true
pytorch
https://paperswithcode.com/paper/cross-modal-contrastive-learning-for-1
Cross-modal Contrastive Learning for Multimodal Fake News Detection
2302.14057
https://arxiv.org/abs/2302.14057v2
https://arxiv.org/pdf/2302.14057v2.pdf
https://github.com/wishever/coolant
true
true
false
pytorch
https://paperswithcode.com/paper/it-doesnt-look-good-for-a-date-transforming
“It doesn’t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation Systems
null
https://aclanthology.org/2021.emnlp-main.145
https://aclanthology.org/2021.emnlp-main.145.pdf
https://github.com/vbursztyn/critique-to-preference-emnlp2021
true
true
false
tf
https://paperswithcode.com/paper/contrastive-aligned-joint-learning-for
Contrastive Aligned Joint Learning for Multilingual Summarization
null
https://aclanthology.org/2021.findings-acl.242
https://aclanthology.org/2021.findings-acl.242.pdf
https://github.com/brxx122/calms
true
true
false
pytorch