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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/MaiqiVerse/AutoGNR

공식 코드

Automated Heterogeneous Network learning with Non-Recursive Message Passing

https://github.com/MalongTech/research-fad

공식 코드

Representation Sharing for Fast Object Detector Search and Beyond

https://github.com/MaxGhi8/HyperNOs

공식 코드

HyperNOs: Automated and Parallel Library for Neural Operators Research

https://github.com/MedMNIST/MedMNIST

공식 코드

MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis

https://github.com/MeherBhaskar/OptiMindTune

공식 코드

OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization

https://github.com/Meituan-AutoML/CPVT

공식 코드

Conditional Positional Encodings for Vision Transformers

https://github.com/Meituan-AutoML/DARTS-

공식 코드

DARTS-: Robustly Stepping out of Performance Collapse Without Indicators

https://github.com/MiaoZhang0525/iDARTS

공식 코드

iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients

https://github.com/MilanShao/AutoML-for-Multi-Class-Anomaly-Compensation-of-Sensor-Drift

공식 코드

AutoML for Multi-Class Anomaly Compensation of Sensor Drift

https://github.com/MohammadSadeghSalehi/ISGD

공식 코드

Bilevel Learning with Inexact Stochastic Gradients

https://github.com/MrtnMndt/meta-learning-CODEBRIM

공식 코드

Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset

https://github.com/Multi-Objective-NAS/self-supervised-nas

공식 코드

Pretraining Neural Architecture Search Controllers with Locality-based Self-Supervised Learning

https://github.com/NVlabs/unas

공식 코드

UNAS: Differentiable Architecture Search Meets Reinforcement Learning

https://github.com/NehzUx/AutoSeries

공식 코드

AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge

https://github.com/NinaMaz/NAS_RL_torch

공식 코드

Optimizing the Neural Architecture of Reinforcement Learning Agents

https://github.com/ORippler/MSD_2018

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AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018

https://github.com/OlegBezr/auto_ml

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A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning Tools

https://github.com/On-Point-RND/SeqNAS

공식 코드

SeqNAS: Neural Architecture Search for Event Sequence Classification

https://github.com/PGijsbers/gama

공식 코드

GAMA: a General Automated Machine learning Assistant

https://github.com/PGijsbers/symbolicdefaults

공식 코드

Meta-Learning for Symbolic Hyperparameter Defaults

https://github.com/PITT-JZ-COOP/Co-Explore-NAS

공식 코드

Hardware/Software Co-Exploration of Neural Architectures

https://github.com/PKU-DAIR/SGL

공식 코드

PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm

https://github.com/PKU-DAIR/mindware

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VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition

https://github.com/PKU-DAIR/open-box

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Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCup

https://github.com/PatrykChrabaszcz/Imagenet32_Scripts

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

https://github.com/Pattio/DeepSwarm

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DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence

https://github.com/PhysicsNAS/PhysicsNAS

공식 코드

Blending Diverse Physical Priors with Neural Networks

https://github.com/Project-MONAI/tutorials/tree/master/automl/DiNTS

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DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation

https://github.com/QB3/sparse-ho

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Implicit differentiation of Lasso-type models for hyperparameter optimization

https://github.com/QQTh4/NAS4Ultrasound

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SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation

https://github.com/QUVA-Lab/COMBO

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Combinatorial Bayesian Optimization using the Graph Cartesian Product

https://github.com/QingbeiGuo/DNAL

공식 코드

Differentiable Neural Architecture Learning for Efficient Neural Network Design

https://github.com/RICE-EIC/HW-NAS-Bench

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HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark

https://github.com/Ren-Research/OneProxy

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One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search

https://github.com/RobGeada/SpiderNet-NTKLRC

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SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics

https://github.com/RobGeada/bonsai-net-lite

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Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners

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