Papers with Code 아카이브
AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 4
- 보존된 평가 기록
- 11
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/jcwleo/random-network-distillation-pytorch
https://github.com/jdromano2/tpot-nn
https://github.com/jekyllstein/HORDOpt.jl
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates
https://github.com/jerermyyoung/rtlearning
Searching to Exploit Memorization Effect in Learning from Corrupted Labels
https://github.com/jerry73204/mobilenet-v3-rs
https://github.com/jfpettit/flare
https://github.com/jhare96/reinforcement-learning
https://github.com/jiangyingjunn/i-darts
https://github.com/jianzhnie/AutoTabular
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
https://github.com/jiaowoguanren0615/MobileNetV4
https://github.com/jiesutd/NCRFpp
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
https://github.com/jiesutd/PyTorchSeqLabel
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
https://github.com/jiesutd/yato
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
https://github.com/jim-schwoebel/allie
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
https://github.com/jiwoongim/OHO
Online hyperparameter optimization by real-time recurrent learning
https://github.com/jkooy/NAS-UDA
https://github.com/jmjeon94/MobileNet-Pytorch
https://github.com/joe-siyuan-qiao/NeuralRejuvenation-CVPR19
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization
https://github.com/johannesu/NASNet-keras
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/jordan-hs/diversity_is_definitely_needed
Once-for-All: Train One Network and Specialize it for Efficient Deployment
https://github.com/jordan-hs/rss-interference-cvprw2022
Once-for-All: Train One Network and Specialize it for Efficient Deployment
https://github.com/js-aguiar/wheat-object-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/jsztompka/MultiAgent-PPO
https://github.com/jsztompka/PPO-demo
https://github.com/juliusfrost/Research-Paper-Implementations
https://github.com/jump-dev/diffopt.jl
Flexible Differentiable Optimization via Model Transformations
https://github.com/junaidaliop/MobileNetV4
https://github.com/jvidals09/Decentralized-and-multi-agent-control-of-Franka-Emika-Panda-robot-in-continuous-task-execution
https://github.com/jw1401/PPO-Tensorflow-2.0
https://github.com/kaileymonn/Quantized-ENAS-ConvNets
https://github.com/kairess/efficientnet_example
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/kalifou/robotics-rl-srl
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
https://github.com/kapllan/zeroshot_lexglue
https://github.com/kayuksel/pytorch-adamaio
https://github.com/kc-ml2/darts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.