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/Navy-RISE-Lab/nn_data_collection

공식 코드

Automatic Generation of Machine Learning Synthetic Data Using ROS

https://github.com/NetworkScienceLab/GDM

공식 코드

Machine learning dismantling and early-warning signals of disintegration in complex systems

https://github.com/Networks-Learning/regression-under-assistance

공식 코드

Regression Under Human Assistance

https://github.com/Networks-Learning/spaced-selection

공식 코드

Large-scale randomized experiment reveals machine learning helps people learn and remember more effectively

https://github.com/NewGround-LLC/psistats

공식 코드

Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality

https://github.com/Ninarehm/attack

공식 코드

Exacerbating Algorithmic Bias through Fairness Attacks

https://github.com/NirantK/Hinglish

공식 코드

HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection

https://github.com/Nkluge-correa/skateboarding-trick-classifier

공식 코드

Development of a skateboarding trick classifier using accelerometry and machine learning

https://github.com/Olauwers/Applicability-and-interpretation-of-the-deterministic-weighted-cepstral-distance

공식 코드

Applicability and interpretation of the deterministic weighted cepstral distance

https://github.com/OlegBezr/auto_ml

공식 코드

A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning Tools

https://github.com/Omkar-Ranadive/Earthquake-Detective

공식 코드

Applying Machine Learning to Crowd-sourced Data from Earthquake Detective

https://github.com/OneClickDeepLearning/classificationOfResidentialRequests

공식 코드

Hybrid Machine Learning Models of Classifying Residential Requests for Smart Dispatching

https://github.com/Open-Catalyst-Project/Open-Catalyst-Dataset

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The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts

https://github.com/OpenBioLink/OpenBioLink

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OpenBioLink: A benchmarking framework for large-scale biomedical link prediction

https://github.com/OpenMined/PSI

공식 코드

Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning

https://github.com/OpenMined/PyDentity/tree/master/projects/aries-fl

공식 코드

Privacy and Trust Redefined in Federated Machine Learning

https://github.com/OpenMined/PyVertical

공식 코드

Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning

https://github.com/OpenMined/TenSEAL

공식 코드

TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption

https://github.com/OptMLGroup/SQN

공식 코드

Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample

https://github.com/OptimusPrimus/dcase2019_task1b

공식 코드

Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification

https://github.com/OxfordML/Fair_Regression

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Intersectionality: Multiple Group Fairness in Expectation Constraints

https://github.com/PASSIONLab/distributed_sddmm

공식 코드

Distributed-Memory Sparse Kernels for Machine Learning

https://github.com/PBGraff/SwiftGRB_PEanalysis

공식 코드

Machine Learning Model of the Swift/BAT Trigger Algorithm for Long GRB Population Studies

https://github.com/PGelss/scikit_tt

공식 코드

Tensor-based algorithms for image classification

https://github.com/PGijsbers/gama

공식 코드

GAMA: a General Automated Machine learning Assistant

https://github.com/PV-Lab/AUTO-XRD

공식 코드

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

https://github.com/Pacmed/aisg_2019

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Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications

https://github.com/PaddlePaddle/PaddleHelix

공식 코드

Docking-based Virtual Screening with Multi-Task Learning

https://github.com/PaddlePaddle/Quantum

공식 코드

VSQL: Variational Shadow Quantum Learning for Classification

https://github.com/Pehlevan-Group/kernel-generalization

공식 코드

Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networks

https://github.com/Pehlevan-Group/sgd_structured_features

공식 코드

Learning Curves for SGD on Structured Features

https://github.com/PennyLaneAI/pennylane

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PennyLane: Automatic differentiation of hybrid quantum-classical computations

https://github.com/PerttuHamalainen/DRMM

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Deep Residual Mixture Models

https://github.com/Petkomat/fuji-score

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Fuzzy Jaccard Index: A robust comparison of ordered lists

https://github.com/PhilJd/freiburg_groceries_dataset

공식 코드

The Freiburg Groceries Dataset

https://github.com/PhilippBach/DoubleMLReplicationCode

공식 코드

DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R

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