Papers with Code 아카이브
BIG-bench Machine Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 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/Featuretools/featuretools-docker
https://github.com/FedML-AI/FedML
https://github.com/Fickle519/stockforecastingmodelbasedon_XGboost
https://github.com/FlorentinCDX/Roundtrip/tree/master/Roundtrip
https://github.com/Flunzmas/vp-suite
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
https://github.com/FynnQ/a-test
Fruit and Vegetable Identification Using Machine Learning for Retail Applications
https://github.com/GCaptainNemo/optimization-project
https://github.com/GISH123/Cathay-Holdings-CIP-Projects-for-Interpretable-Machine-Learning
Data Shapley: Equitable Valuation of Data for Machine Learning
https://github.com/GZQ0723/YoloV4
https://github.com/Gapeh/onePixelAttack
https://github.com/Gavino7/YOLOv3set
https://github.com/GeeGeeWP/SCML
https://github.com/GeorgeLuImmortal/Adaptive-Tuning-Active-Learning
https://github.com/GeorgeLuImmortal/Effectiveness-of-Pretrained-Transformer-based-Language-Models-in-Active-Learning-for-Labelling-Data
https://github.com/GeorgeTzannetos/badnets
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
https://github.com/GeorgeTzannetos/ml-leaks-pytorch
https://github.com/GeorgiosSmyrnis/multiclass_minimization_icml2020
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/GiangTTran/SparseRandomFeatureExpansion
https://github.com/Giully314/Endurance
https://github.com/GrandGarcon/Complete_Cybersecurity_Path
Bridging machine learning and cryptography in defence against adversarial attacks
https://github.com/GuoQuanhao/YOLOv4-Paddle
https://github.com/GustikS/NeuraLogic
https://github.com/Guzpenha/slice_based_learning
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
https://github.com/HIPS/Spearmint
Practical Bayesian Optimization of Machine Learning Algorithms
https://github.com/HSE-LAMBDA/supernovae_classification
Photometric Data-driven Classification of Type Ia Supernovae in the Open Supernova Catalog
https://github.com/Habush/nam_jax
Neural Additive Models: Interpretable Machine Learning with Neural Nets
https://github.com/Hardly-Human/Yolo-Object-Detector
https://github.com/HazyResearch/metal
https://github.com/HazyResearch/snorkel
https://github.com/HeegonJin/yolov1
https://github.com/Hem7513/Decision-Trees-and-XGBoost-Algorithm-Documentation
https://github.com/HeuJulien/ML-P6-2020
A high-bias, low-variance introduction to Machine Learning for physicists
https://github.com/Hitsaa/Practice
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/HowToMakeABomb101/Hot2MakeAB0mbSite
https://github.com/Huawei-MRC-OSI/optml-zoo
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.