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AutoML

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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/TravisLeeTS/grabcvchallenge

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/TreeLimes/QANAS

Neural Architecture Search with Reinforcement Learning

https://github.com/UTS-AAi/autoweka

AutonoML: Towards an Integrated Framework for Autonomous Machine Learning

https://github.com/UesugiErii/tf2-PPO-atari

Proximal Policy Optimization Algorithms

https://github.com/VITA-Group/AutoGAN

AutoGAN: Neural Architecture Search for Generative Adversarial Networks

https://github.com/VITA-Group/AutoSpeech

AutoSpeech: Neural Architecture Search for Speaker Recognition

https://github.com/VainF/Torch-Pruning

Learning Efficient Convolutional Networks through Network Slimming

https://github.com/VinayBhupalam/melanoma-detection

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/WillButAgain/ENAS

Efficient Neural Architecture Search via Parameter Sharing

https://github.com/WonJunPark/Efficientnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/XinbangZhang/DSO-NAS

You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization

https://github.com/YaCpotato/B4ResearchDeepaugment

Neural Architecture Search with Reinforcement Learning

https://github.com/YaCpotato/deepaugmentFix

Neural Architecture Search with Reinforcement Learning

https://github.com/YushiChen/Auto-CNN-HSI-Classification

DARTS: Differentiable Architecture Search

https://github.com/ZJCV/NetworkSlimming

Learning Efficient Convolutional Networks through Network Slimming

https://github.com/ZTao-z/ProxylessNAS

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

https://github.com/ZachZhu7/banana-git

BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search

https://github.com/ZackPashkin/YOLOv3-EfficientNet-EffYolo

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/Zartris/TD3_continuous_control

Proximal Policy Optimization Algorithms

https://github.com/ZilinGao/GM-SOP

A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

https://github.com/aaditkapoor/PDFExtract

HAT: Hardware-Aware Transformers for Efficient Natural Language Processing

https://github.com/aai-institute/pyDVL

Nystrom Method for Accurate and Scalable Implicit Differentiation

https://github.com/abcp4/DAPytorch

Neural Architecture Search with Reinforcement Learning

https://github.com/abcp4/MyDarts

DARTS: Differentiable Architecture Search

https://github.com/abcp4/autokeras

Auto-Keras: An Efficient Neural Architecture Search System

https://github.com/abhoi/Keras-MnasNet

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/abhuse/pytorch-efficientnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/adammenges/DARTS-PyTorch

DARTS: Differentiable Architecture Search

https://github.com/adamos581/ppo-keras-football

Proximal Policy Optimization Algorithms

https://github.com/adik993/ppo-pytorch

Proximal Policy Optimization Algorithms

https://github.com/agosiewska/SAFE-experiments

Lifting Interpretability-Performance Trade-off via Automated Feature Engineering

https://github.com/agupta231/CARROL

Are GANs Created Equal? A Large-Scale Study

https://github.com/ahundt/renas

Efficient Neural Architecture Search via Parameter Sharing

https://github.com/ailab-pku/rl-framework

Proximal Policy Optimization Algorithms

https://github.com/akrapukhin/MobileNetV3

Searching for MobileNetV3

https://github.com/alecfilios/Training-Intelligent-game-Agents-through-Competitive-Reinforcement-Learning

Proximal Policy Optimization Algorithms

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.