AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 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/sdkchris/Exercise-Sheet-1
https://github.com/sdxzy/LPM-PyTorch-implementation
https://github.com/seermer/TensorFlow2-EfficientNetV2
https://github.com/semskurto/APTOS
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/sense-x/tokenmix
UniNet: Unified Architecture Search with Convolution, Transformer, and MLP
https://github.com/senthilva/Keras_functional_API_CNN
https://github.com/sequential-parameter-optimization/spotgui
https://github.com/seriousssam/deep-image-prior-transfer
https://github.com/setharram/facenet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/seulkiyeom/once-for-all
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/seunghwan1228/CNN_EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/seungjaeryanlee/osim-rl-helper
https://github.com/seungwonpark/RandWireNN
Exploring Randomly Wired Neural Networks for Image Recognition
https://github.com/sevakon/efficientdet
https://github.com/sgonzalez/SwiftCMA
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization
https://github.com/sgonzalez/SwiftGenetics
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization
https://github.com/shawon100/Web-Link-Prediction-System
Web Links Prediction And Category-Wise Recommendation Based On Browser History
https://github.com/shchur/automlbenchmark
https://github.com/shijianjian/efficientnet-pytorch-3d
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/shiningsunnyday/mcts-chess
https://github.com/shivam-raj/detector
https://github.com/showlo/mobilenetv3
https://github.com/shreyesss/PPO-implementation-keras-tensorflow
https://github.com/shuishida/soaprl
https://github.com/siddharthverma314/proximalpolicyoptimization
https://github.com/siddikui/Fast-DARTS
https://github.com/siddikui/ProgressiveDARTS
Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation
https://github.com/signatrix/efficientdet
https://github.com/silvialuu/DRL-2018
https://github.com/simon0987/ACGAN-implemented-with-pytorch
https://github.com/sinamoradian/SNIP-NAS-2
https://github.com/sirakik/mprg_fc
https://github.com/sirakik/ppo_football
https://github.com/sjoshi804/hierarchical-darts-neural-architecture-search
Hierarchical Representations for Efficient Architecture Search
https://github.com/skhetarpal/Prostate_Cancer_Grade_Assessment
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.