Papers with Code 아카이브

BIG-bench Machine Learning

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하위 태스크: 0
평가표: 2
보존된 평가 기록: 2

This branch include most common machine learning fundamental algorithms.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/HolyBayes/pytorch_ard

공식 코드

Variational Dropout Sparsification for Particle Identification speed-up

https://github.com/HongshengHu/membership-inference-machine-learning-literature

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Membership Inference Attacks on Machine Learning: A Survey

https://github.com/HuiHu1/Distributed-Private-Fair-Learning

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A Distributed Fair Machine Learning Framework with Private Demographic Data Protection

https://github.com/Hyeongmin-Cho/Efficient-Data-Quality-Measures-for-High-Dimensional-Classification-Data

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Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

https://github.com/IBM/OoD/tree/master/IRM_games

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Invariant Risk Minimization Games

https://github.com/IBM/Project_CodeNet

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CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks

https://github.com/IBM/UAE

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Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning

https://github.com/IBM/adaptive-federated-learning

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Adaptive Federated Learning in Resource Constrained Edge Computing Systems

https://github.com/IBM/adversarial-robustness-toolbox

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Adversarial Robustness Toolbox v1.0.0

https://github.com/IBM/ai-privacy-toolkit

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Anonymizing Machine Learning Models

https://github.com/IBM/devil-in-GAN

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The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models

https://github.com/IBM/federated-learning-lib

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IBM Federated Learning: an Enterprise Framework White Paper V0.1

https://github.com/IBM/sensitive-subspace-robustness

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Training individually fair ML models with Sensitive Subspace Robustness

https://github.com/IITB-OpenOCRCorrect/iitb-openocr-digit-tool

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UDAAN: Machine Learning based Post-Editing tool for Document Translation

https://github.com/IPL-UV/mlregocean

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Retrieval of Case 2 Water Quality Parameters with Machine Learning

https://github.com/IRC-SPHERE/HyperStream

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HyperStream: a Workflow Engine for Streaming Data

https://github.com/ISorokos/SafeML

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SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure

https://github.com/ITMO-NSS-team/oil-field-modelling

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Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea

https://github.com/IliasZadik/double_orthogonal_ml

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Orthogonal Machine Learning: Power and Limitations

https://github.com/Imathatguy/Acceptance-Region

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On the Resilience of Biometric Authentication Systems against Random Inputs

https://github.com/ImperialCollegeLondon/polymer_blend_morphology

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Numerical simulation, clustering and prediction of multi-component polymer precipitation

https://github.com/InsaneMonster/pasqualini2019prngrl

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Pseudo Random Number Generation: a Reinforcement Learning approach

https://github.com/IntelLabs/vdms

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VDMS: Efficient Big-Visual-Data Access for Machine Learning Workloads

https://github.com/IvanLetteri/MTA-KDD-19

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Dataset Optimization Strategies for MalwareTraffic Detection

https://github.com/JRice15/physics-informed-autoencoders

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Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting

https://github.com/James-Yuichi-Sato/MLSYS-2022-Video-Pipeline

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Video-Data Pipelines for Machine Learning Applications

https://github.com/JanAckmann/MLPrecon

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Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flows

https://github.com/JesperDramsch/4D-seismic-neural-inversion

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Including Physics in Deep Learning -- An example from 4D seismic pressure saturation inversion

https://github.com/JesperDramsch/70-Years-of-Machine-Learning-in-Geoscience-in-Review

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70 years of machine learning in geoscience in review

https://github.com/JesperDramsch/Complex-CNN-Seismic

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Complex-valued neural networks for machine learning on non-stationary physical data

https://github.com/JiahuiKChen/Practical-Configurable-Network-Traffic-Classification

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Practical and Configurable Network Traffic Classification Using Probabilistic Machine Learning

https://github.com/JianqiaoMao/Interpretable-machine-learning-for-HVAC-predictive-control

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Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic Buildings

https://github.com/Jin93/Multi-Resolution-SL

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Multi-resolution Super Learner for Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI

https://github.com/JohnNay/forecastVeg

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A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health

https://github.com/JohnSnowLabs/spark-nlp

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Spark NLP: Natural Language Understanding at Scale

https://github.com/JonasLandman/quantum_kmeans_NeurIPS_2019

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q-means: A quantum algorithm for unsupervised machine learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.