AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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://gitlab.com/hyunp2/ai4molcryst_argonne
공식 코드End-to-end AI framework for interpretable prediction of molecular and crystal properties
https://gitlab.com/viper-purdue/lic-pruning
공식 코드Structured Pruning and Quantization for Learned Image Compression
https://huggingface.co/spaces/herilalaina/ifbo
공식 코드In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
http://github.com//xingchenwan/cell-based-NAS-analysis
On Redundancy and Diversity in Cell-based Neural Architecture Search
https://bitbucket.org/act-lab/release
https://gitee.com/ncucjm/GraphPAS
GraphPAS: Parallel Architecture Search for Graph Neural Networks
https://github.com/0h-n0/tfdbonas
https://github.com/0h-n0/thdbonas
https://github.com/170928/-Review-Generative-Adversarial-Imitation-Learning
https://github.com/170928/-Review-Proximal-Policy-Optimization-Algorithms
https://github.com/2023-MindSpore-1/ms-code-1
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search
https://github.com/2023-MindSpore-1/ms-code-151
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/adelaide_ea
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
https://github.com/2023-MindSpore-1/ms-code-17/tree/main/AutoSlim
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
https://github.com/2023-MindSpore-1/ms-code-174
https://github.com/2023-MindSpore-1/ms-code-18/tree/main/ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/2023-MindSpore-1/ms-code-185
https://github.com/2023-MindSpore-1/ms-code-186
https://github.com/2023-MindSpore-1/ms-code-189/tree/main/modelarts
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-19
https://github.com/2023-MindSpore-1/ms-code-193
https://github.com/2023-MindSpore-1/ms-code-195
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-21
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/2023-MindSpore-1/ms-code-212/tree/main/EfficientDet_d0
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HourNAS
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PGAN
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pnasnet
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/proxylessnas
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnet50_bam
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/sr_ea
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
https://github.com/2023-MindSpore-1/ms-code-224/tree/main/adelaide_ea
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
https://github.com/2023-MindSpore-4/Code-5/tree/main/HourNAS
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
https://github.com/2023-MindSpore-4/Code-5/tree/main/ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/2023-MindSpore-4/Code-5/tree/main/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.