Papers with Code 아카이브
BIG-bench Machine Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/owkin/grad-cnns
https://github.com/oxfordinternetinstitute/oxonfair
Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems
https://github.com/oxquantum-repo/AutoDot
Machine learning enables completely automatic tuning of a quantum device faster than human experts
https://github.com/oxquantum-repo/CVAE_for_QE
Efficiently measuring a quantum device using machine learning
https://github.com/oxquantum-repo/Tuning_old
Machine learning enables completely automatic tuning of a quantum device faster than human experts
https://github.com/oxquantum/CVAE
Efficiently measuring a quantum device using machine learning
https://github.com/oxquantum/CVAE_for_QE
Efficiently measuring a quantum device using machine learning
https://github.com/oyeabhijit/yolo-obj-detection
https://github.com/ozolotareva/Flimma
HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning
https://github.com/pacifinapacific/awesome_sportanalysis_paper
Game Plan: What AI can do for Football, and What Football can do for AI
https://github.com/pair-code/datacardsplaybook
https://github.com/palomapiot/metahate
https://github.com/panchambanerjee/Word_embedding_biases
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
https://github.com/parthapratimbanik/facial-keypoint-detection-udacity-ppb
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
https://github.com/parviagrawal/IdenProf
https://github.com/pasquini-dario/PLR
https://github.com/pasta41/deception
The Marginal Value of Adaptive Gradient Methods in Machine Learning
https://github.com/paszin/paszin.github.io
Data-driven HR - Résumé Analysis Based on Natural Language Processing and Machine Learning
https://github.com/patel-zeel/coreset
https://github.com/paullerner/viquae
WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
https://github.com/pavitrakumar78/Playing-custom-games-using-Deep-Learning
https://github.com/pdefraene/cgp_cnn_predictors
Fast and Reliable Architecture Selection for Convolutional Neural Networks
https://github.com/pdghawk/systrade
API design for machine learning software: experiences from the scikit-learn project
https://github.com/peaclab/comte
Counterfactual Explanations for Machine Learning on Multivariate Time Series Data
https://github.com/peanutshawny/lstm-stock-predictor
https://github.com/pehersto/reproj
Learning physics-based reduced-order models for a single-injector combustion process
https://github.com/penglert367/madminer
MadMiner: Machine learning-based inference for particle physics
https://github.com/pennswood/fairlay-ml
https://github.com/pennylaneai/generative-quantum-states
Provably efficient machine learning for quantum many-body problems
https://github.com/persiaml/persia
https://github.com/petteriTeikari/mixedImageSeparation
https://github.com/pevnak/JsonGrinder.jl
https://github.com/pevnak/Mill.jl
https://github.com/pfistfl/autoxgboostMC
https://github.com/phamquiluan/ResidualMaskingNetwork
Challenges in Representation Learning: A report on three machine learning contests
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.