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/tianbaochou/CNAS
https://github.com/tiangexiang/BiX-NAS
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation
https://github.com/tidedra/vl-rlhf
https://github.com/tilkb/thermoai
https://github.com/timctho/random-wired-nn-tensorflow
Exploring Randomly Wired Neural Networks for Image Recognition
https://github.com/tinhb92/rnn_darts_fastai
https://github.com/titu1994/keras-efficientnets
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/titu1994/neural-architecture-search
https://github.com/titu1994/progressive-neural-architecture-search
https://github.com/tmjeong1103/RL_with_RAY
https://github.com/toanbkmt/EfficientnetFruitDetect
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/toandaominh1997/EfficientDet.Pytorch
https://github.com/tobiasemrich/SchafkopfRL
https://github.com/tommyvsfu1/RL-NTU
https://github.com/toni-sm/skrl
https://github.com/toniesteves/cnn-bayesian-optimization
https://github.com/topdu/openocr
AutoSTR: Efficient Backbone Search for Scene Text Recognition
https://github.com/towhee-io/towhee
MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/tqichun/auto-sklearn-homework
https://github.com/triple7/Keras-WGAN-RGB-128x128
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/tsing-cv/EfficientNet-tensorflow-eager
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ttruty/facial-feature-mouse-control
https://github.com/tuanio/AutoRec
https://github.com/tuanpnm99/RLPongAgent
https://github.com/tuggeluk/pycls
https://github.com/twice154/ofa-for-super-resolution
Once-for-All: Train One Network and Specialize it for Efficient Deployment
https://github.com/uber-research/GTN
https://github.com/uber-research/Synthetic-Petri-Dish
Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search
https://github.com/uber/ludwig
Personalized Benchmarking with the Ludwig Benchmarking Toolkit
https://github.com/ucr-optml/fedmsa
Optimizing Millions of Hyperparameters by Implicit Differentiation
https://github.com/ujsyehao/mobilenetv3-ssd
https://github.com/ultmaster/neuralpredictor.pytorch
https://github.com/ultralytics/yolov5
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/uma-pi1/dist-kge
Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings
https://github.com/uvipen/super-mario-bros-ppo-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.