AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)
Source: CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Gouet/Acrobot-PPO
https://github.com/Gouet/Breakout-V0
https://github.com/Gouet/PPO-gym
https://github.com/Gouet/PPO-pytorch
https://github.com/Gregory-Eales/proximal-policy-optimization
https://github.com/GuHongyang/AM-LFS-PyTorch
https://github.com/Guang000/Awesome-Dataset-Distillation/blob/main/README.md
https://github.com/Guillem96/efficientdet-tf
https://github.com/GuoQuanhao/EfficientDet-Paddle
https://github.com/HIPS/Spearmint
Practical Bayesian Optimization of Machine Learning Algorithms
https://github.com/HO4X/TSR_JetsonTX2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/HaiTMai/Time-Series-Forecast
https://github.com/HaoyueBaiZJU/NAS-OoD
NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization
https://github.com/HardLaugh/EfficientDet-bifpn
https://github.com/HeungChangLee/AutoML
https://github.com/HyeonhoonLee/MAIC2021_Sleep
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/IMvision12/keras-vision-models
https://github.com/IRES-FAU/Evolutionary-Architecture-Search-for-Graph-Neural-Networks
https://github.com/InSpaceAI/RL-Zoo
https://github.com/Insaniauser/ViPNAS_mindspore
ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search
https://github.com/Ipsedo/EvoMotion
https://github.com/Ipsedo/TabPFN
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
https://github.com/IsmaelCesar/darts
https://github.com/JL321/Proximal-Policy-Optimization
https://github.com/JacobM184/EfficientNet-for-Gun-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/JagadishSivakumar/GlobalWheatDetection
https://github.com/JasperSnoek/spearmint
Practical Bayesian Optimization of Machine Learning Algorithms
https://github.com/JensSettelmeier/EfficientDet-DeepSORT-Tracker
https://github.com/JeongwookUm/TEST_AutoSpeech-master
AutoSpeech: Neural Architecture Search for Speaker Recognition
https://github.com/JiaminRen/RandWireNN
Exploring Randomly Wired Neural Networks for Image Recognition
https://github.com/JihaoLee/Randomly_Wired_reproducibility
Exploring Randomly Wired Neural Networks for Image Recognition
https://github.com/JinLi711/Attention-Augmented-Convolution
https://github.com/JinLi711/Convolution_Variants
https://github.com/Jintao-Huang/EfficientDet_PyTorch
https://github.com/Jintao-Huang/EfficientNet_PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.