AutoML
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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/2023-MindSpore-4/Code-5/tree/main/mobilenetv3_large
https://github.com/2023-MindSpore-4/Code-5/tree/main/nas-fpn
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/2023-MindSpore-4/Code11/tree/main/retinanet_resnet152
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code11/tree/main/single_path_nas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/2023-MindSpore-4/Code11/tree/main/sr_ea
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
https://github.com/2023-MindSpore-4/Code14/tree/main/SpineNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/resnet50_bam
https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet101
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet152
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code3/tree/main/EfficientDet_d0
https://github.com/2023-MindSpore-4/Code3/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-4/Code7/tree/main/single_path_nas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/2023-MindSpore-4/Code7/tree/main/sr_ea
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
https://github.com/2023-MindSpore-4/Code8/tree/main/Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/2023-MindSpore-4/Code8/tree/main/AutoSlim
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
https://github.com/2023-MindSpore-4/Code9/tree/main/RetinaNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/gpt
https://github.com/2024-MindSpore-1/Code4/tree/main/Efficientnet
https://github.com/2024-MindSpore-1/Code4/tree/main/MobileNet/mobilenetv3
https://github.com/2024-MindSpore-1/Code5/tree/main/ArbitraryStyleTransfer
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/2024-MindSpore-1/Code6/tree/main/ArbitraryStyleTransfer
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/2024-MindSpore-1/Code6/tree/main/Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/2024-MindSpore-1/Code6/tree/main/AutoSlim
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
https://github.com/2mawi2/master-thesis-experiments
https://github.com/6210612757/facerecognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ASzot/ppo-pytorch
https://github.com/ActiveVisionLab/NUQ
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes
https://github.com/Adlik/model_optimizer
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
https://github.com/AhmadQasim/proxylessnas-dense
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/AlessandraFaria/autosklearn
https://github.com/AlvinIsonomia/darts.pytorch1.3_MultiGPU
https://github.com/AmeerHajAli/ray2
Tune: A Research Platform for Distributed Model Selection and Training
https://github.com/AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/An0wn/machinelearning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.