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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/morrisxu-driving/video-music_cross-modal_retrival

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/moukamisama/recon

Automated Search for Resource-Efficient Branched Multi-Task Networks

https://github.com/mr-eggplant/adaxpert0

Single Path One-Shot Neural Architecture Search with Uniform Sampling

https://github.com/mrzzy/np-dl-assign-1

Learning Transferable Architectures for Scalable Image Recognition

https://github.com/mseitzer/pytorch-fid

Are GANs Created Equal? A Large-Scale Study

https://github.com/msuzen/bristol

Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search

https://github.com/murdockhou/Yet-Another-EfficientDet-Pytorch-Convert-ONNX-TVM

EfficientDet: Scalable and Efficient Object Detection

https://github.com/mvenouziou/Project-Attention-Is-What-You-Get

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/mvpzhangqiu/yolov5prune

Learning Efficient Convolutional Networks through Network Slimming

https://github.com/najlaeLemrabet/FacialKeypointsDetection

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

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

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

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

DARTS: Differentiable Architecture Search

https://github.com/nassimmokhtari/Improved-FireFly-Algorithm

https://github.com/nazarov-yuriy/zh-ru-shared-task

The Evolved Transformer

https://github.com/near32/regym

Proximal Policy Optimization Algorithms

https://github.com/neeraj-j/MixNet

MixConv: Mixed Depthwise Convolutional Kernels

https://github.com/negrinho/deep_architect_legacy

DeepArchitect: Automatically Designing and Training Deep Architectures

https://github.com/neuraloperator/gano

Generative Adversarial Neural Operators

https://github.com/newwhitecheng/swiftnet

SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures

https://github.com/nginyc/rafiki

Rafiki: Machine Learning as an Analytics Service System

https://github.com/nikdnaik/accelerating_nas-1

Accelerating Neural Architecture Search using Performance Prediction

https://github.com/nikhilbarhate99/PPO

Proximal Policy Optimization Algorithms

https://github.com/nikhilbarhate99/PPO-PyTorch

Proximal Policy Optimization Algorithms

https://github.com/nikita270a/BayesianDARTS

DARTS: Differentiable Architecture Search

https://github.com/nimiew/Grab-Computer-Vision

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/nitthilan/pommerman

Proximal Policy Optimization Algorithms

https://github.com/nlinc1905/evolutionary-reinforcement-learner

Neural Architecture Search without Training

https://github.com/noahho/caafe

Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

https://github.com/northeastsquare/effficientnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/nric/ProximalPolicyOptimizationContinuousKeras

Proximal Policy Optimization Algorithms

https://github.com/nsarang/MnasNet

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/nutellamok/advrush

DARTS: Differentiable Architecture Search

https://github.com/nvidia/modulus

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach

https://github.com/nvidia/physicsnemo

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach

https://github.com/nvlabs/gbrl_sb3

Proximal Policy Optimization Algorithms

https://github.com/ofsoundof/Heterogeneity_Hypothesis

DHP: Differentiable Meta Pruning via HyperNetworks

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