https://github.com/ModelOriented/rSAFE
SAFE ML: Surrogate Assisted Feature Extraction for Model Learning
Papers with Code 아카이브
AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/MohammadFebriyanto/ahuahu_app
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice
https://github.com/MrD1360/Keras-implementation-of-MobileNetv3-with-L-RASSP
https://github.com/MrSyee/pg-is-all-you-need
https://github.com/NACLab/robust-active-inference
https://github.com/NVIDIA/DeepLearningExamples
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Narsil/rl-baselines
https://github.com/NatGr/Master_Thesis
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
https://github.com/Nathnos/Article-Reproduction-Deep-Learning-Efficient-Joint-Neural-Architecture-and-Hyperparameter-Search
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search
https://github.com/NervanaSystems/coach
https://github.com/NervanaSystems/distiller
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/NivNayman/XNAS
https://github.com/NoamRosenberg/autodeeplab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/Nordeus/heroic-rl
https://github.com/OFANAS/OFANAS_PerformanceEstimation
Generalized Latency Performance Estimation for Once-For-All Neural Architecture Search
https://github.com/OctThe16th/PPO-Keras
https://github.com/OctopusMind/RLHF_PPO
https://github.com/OniroAI/Instance-segmentation-with-MobileNetV3
https://github.com/Ostyk/walk-bot
https://github.com/PaddlePaddle/PaddleClas
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleOCR
https://github.com/PatrickZH/Improved-Deep-Leakage-from-Gradients
https://github.com/Pointcept/Pointcept
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/Pol22/NAS_DIP
https://github.com/PolinaDruzhinina/mobilenet
https://github.com/PolinaDruzhinina/modelnet
https://github.com/PotatoSpudowski/CactiNet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/Prev/downsampled-imagenet-path-fixer
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
https://github.com/Przemo23/Reversing_Gradient_Differentiation_NN
Gradient-based Hyperparameter Optimization through Reversible Learning
https://github.com/PurityFan/bohb_in_nni
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
https://github.com/RAIVNLab/STR
https://github.com/Rafael1s/Deep-Reinforcement-Learning-Udacity
https://github.com/Rainymood/Gradient-Descent-The-Ultimate-Optimizer
https://github.com/RamposhPidrov/waifu2face
https://github.com/Randl/MobileNetV3-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.