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/code-implementation1/Code1/tree/main/EF-ENAS
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search Spaces
https://github.com/code-implementation1/Code1/tree/main/EfficientDet_d0
https://github.com/code-implementation1/Code3/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/code-implementation1/Code4/tree/main/HourNAS
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
https://github.com/code-implementation1/Code4/tree/main/ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/code-implementation1/Code5/tree/main/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/code-implementation1/Code6/tree/main/PDarts
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild
https://github.com/code-implementation1/Code6/tree/main/nas-fpn
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/code-implementation1/Code6/tree/main/nasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/code-implementation1/Code6/tree/main/pnasnet
https://github.com/code-implementation1/Code6/tree/main/proxylessnas
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/code-implementation1/Code7/tree/main/resnet50_bam
https://github.com/code-implementation1/Code7/tree/main/retinanet_resnet101
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/code-implementation1/Code8/tree/main/SpineNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/code-implementation1/Code8/tree/main/single_path_nas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/codelion/openevolve
AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment
https://github.com/countif/enas_nni
https://github.com/cranial-xix/bome
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
https://github.com/crcrpar/benchmark-runner-ci
Optuna: A Next-generation Hyperparameter Optimization Framework
https://github.com/crcrpar/ci-example-execution
Optuna: A Next-generation Hyperparameter Optimization Framework
https://github.com/crcrpar/optuna-mirror
Optuna: A Next-generation Hyperparameter Optimization Framework
https://github.com/cshannonn/blackscholes_nas
https://github.com/cuihu1998/EfficientDet-d0
https://github.com/curryandsun/AIOL
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
https://github.com/cvtower/SeesawNet-pytorch-reimplement
Seesaw-Net: Convolution Neural Network With Uneven Group Convolution
https://github.com/cvtower/SeesawNet_pytorch
Seesaw-Net: Convolution Neural Network With Uneven Group Convolution
https://github.com/cyrilminaeff/MobileNet
https://github.com/d-li14/efficientnetv2.pytorch
https://github.com/d-li14/mobilenetv3.pytorch
https://github.com/daekeun-ml/autogluon-on-aws
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
https://github.com/danelee2601/rl-based-automatic-berthing
https://github.com/danielpatrickhug/Research_Paper_Parser
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/danstowell/insect_classifier_GDSC23_insecteffnet
https://github.com/darya-baranovskaya/keyword_spotting
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/datamllab/autokaggle
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.