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/alexandonian/pretorched-x
https://github.com/alexbaumi/PPO-Algorithm
https://github.com/alexbaumi/PPO-Algorithms
https://github.com/alexgcsa/AutoMLC
A Robust Experimental Evaluation of Automated Multi-Label Classification Methods
https://github.com/alexmlamb/blocks_rl_gru_setup
https://github.com/algorine/nokore
Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural Networks
https://github.com/alibaba/EasyCV
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/alibaba/EasyTransfer/tree/master/scripts/knowledge_distillation/adabert
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search
https://github.com/alibaba/esim-response-selection
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/alililia/ascend_ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/alililia/ms_extend/tree/main/gpu_efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/alirezakazemipour/ppo-rnd
https://github.com/alisafaya/SHA-RNN.jl
https://github.com/alphadl/darts.pytorch1.1
https://github.com/amanda-lambda/drl-experiments
https://github.com/amanda-lambda/hack-flappy-bird-drl
https://github.com/amartyamukherjee/ppo-packcooling
https://github.com/amaudruz/RL_openaigym
https://github.com/ambroggi/pruning-project-for-deep-neural-networks
DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search
https://github.com/amirdy/dog-breed-classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/amzoyang/CS-221-Final-Project
https://github.com/analogdevicesinc/ai8x-training
Once-for-All: Train One Network and Specialize it for Efficient Deployment
https://github.com/andychinka/dcase-challenge
Tune: A Research Platform for Distributed Model Selection and Training
https://github.com/andyljones/zonotable
https://github.com/anonDEHB/DEHB
Regularized Evolution for Image Classifier Architecture Search
https://github.com/anonNo2/MulTeacher-KD
https://github.com/anonymousest/DBSN
Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure
https://github.com/anorthman/custom
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
https://github.com/anthonysong98/super-mario-bros-ppo
https://github.com/antonFJohansson/Bayesian-Learning-of-Neural-Network-Architectures
https://github.com/araffin/sbx
https://github.com/aragakiyuiii/Gumbel-darts-master
https://github.com/aragakiyuiii/gumbel-pdarts-master
Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation
https://github.com/arberzela/EfficientNAS
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search
https://github.com/ardeal/EfficientNetV2
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.