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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/xkp793003821/PC-DARTS-COOPER

PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search

https://github.com/xkp793003821/autokeras_cooper

Auto-Keras: An Efficient Neural Architecture Search System

https://github.com/xmu-xiaoma666/External-Attention-pytorch

BAM: Bottleneck Attention Module

https://github.com/xslidi/EfficientNets_ddl_apex

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/xtma/simple-pytorch-rl

Proximal Policy Optimization Algorithms

https://github.com/xuanhungho/nsga

Regularized Evolution for Image Classifier Architecture Search

https://github.com/xuannianz/EfficientDet

EfficientDet: Scalable and Efficient Object Detection

https://github.com/xufanxiong/classification-of-caltech-256

Learning Transferable Architectures for Scalable Image Recognition

https://github.com/xwu6614555/MobileNetV3-Mxnet

Searching for MobileNetV3

https://github.com/xxlya/COS598D_Assignment1

Network Pruning via Transformable Architecture Search

https://github.com/yakhyo/head-pose-estimation

Searching for MobileNetV3

https://github.com/yan-roo/SpineNet-Pytorch

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/yangkunpeng-coder/EfficientDet_libtorch

EfficientDet: Scalable and Efficient Object Detection

https://github.com/yangsenius/Pose-Neural-Fabrics-Search

Pose Neural Fabrics Search

https://github.com/yangyucheng000/Paper-2/tree/main/SharpDRO-ms-main

sharpDARTS: Faster and More Accurate Differentiable Architecture Search

https://github.com/yangyucheng000/University/tree/main/model-2/gpt_bigcode

GPT Takes the Bar Exam

https://github.com/yangyucheng000/ViPNAS

ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search

https://github.com/yangyucheng000/mnasnet/blob/main/mnasnet.py

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/yanpei18345156216/BayesianOptimization

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/yashkant/ENAS-Quantized-Neural-Networks

Efficient Neural Architecture Search via Parameter Sharing

https://github.com/yashkant/PNAS-Binarized-Neural-Networks

Progressive Neural Architecture Search

https://github.com/yawen-d/transnasbench

TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search

https://github.com/yiwang89/fmfn-BayesianOptimization

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/yiyuezhuo/A-Tutorial-on-Bayesian-Optimization-exmaple

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/yoavalon/Quadcopter-env

Proximal Policy Optimization Algorithms

https://github.com/yoavalon/QuadcopterReinforcementLearning

Proximal Policy Optimization Algorithms

https://github.com/yoomambo/BayesianOptimization_Tuned

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/youngwoo-yoon/Practical-tips-for-lightweight-deep-learning

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/yqian4/optuna

Optuna: A Next-generation Hyperparameter Optimization Framework

https://github.com/yuke93/ReconfigISP

ReconfigISP: Reconfigurable Camera Image Processing Pipeline

https://github.com/yuz1wan/video_distillation

Dataset Condensation with Gradient Matching

https://github.com/zatang007/BayesianOptimization

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/zbyte64/pytorch-dagsearch

Efficient Neural Architecture Search via Parameter Sharing

https://github.com/zh320/realtime-semantic-segmentation-pytorch

Rethinking Dilated Convolution for Real-time Semantic Segmentation

https://github.com/zhangximing666/Auto-DeepLab_MS

Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation

https://github.com/zhaoyuesun/phee

Automated Concatenation of Embeddings for Structured Prediction

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