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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/yu-zhewen/svd-nas

공식 코드

SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture Search

https://github.com/yucornetto/CAKES

공식 코드

CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks

https://github.com/yuezuegu/UBoostNAS

공식 코드

U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search

https://github.com/yuhuixu1993/PC-DARTS

공식 코드

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

https://github.com/yukang2017/RENAS

공식 코드

Reinforced Evolutionary Neural Architecture Search

https://github.com/yxzwang/boostpfn

공식 코드

Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners

https://github.com/yygle/HybridFormer

공식 코드

HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanism

https://github.com/yyj1ang/mfpsnet

공식 코드

Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration

https://github.com/yyxiongzju/RCNet

공식 코드

Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?

https://github.com/yzhao062/metaod

공식 코드

Automating Outlier Detection via Meta-Learning

https://github.com/zaccharieramzi/hoag

공식 코드

SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models

https://github.com/zaccharieramzi/shine

공식 코드

SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models

https://github.com/zehao-dong/pace

공식 코드

PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs

https://github.com/zehsilva/prior-predictive-specification

공식 코드

Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching

https://github.com/zerocostptnas/zerocost_operation_score

공식 코드

Zero-Cost Operation Scoring in Differentiable Architecture Search

https://github.com/zgornel/knowledge-driven-automl

공식 코드

A knowledge-driven AutoML architecture

https://github.com/zhengsh123/dclp

공식 코드

DCLP: Neural Architecture Predictor with Curriculum Contrastive Learning

https://github.com/zhengxiawu/rethinking_performance_estimation_in_NAS

공식 코드

Rethinking Performance Estimation in Neural Architecture Search

https://github.com/zhidilin/gpssmproj

공식 코드

Recursive Gaussian Process State Space Model

https://github.com/zhoubenjia/RAAR3DNet

공식 코드

Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition

https://github.com/zhoudaquan/rethinking_bottleneck_design

공식 코드

Rethinking Bottleneck Structure for Efficient Mobile Network Design

https://github.com/zhuang-group/spvit

공식 코드

Pruning Self-attentions into Convolutional Layers in Single Path

https://github.com/zhuxunyu/OPP-DARTS

공식 코드

Operation-level Progressive Differentiable Architecture Search

https://github.com/zifuwanggg/mrf-unets

공식 코드

MRF-UNets: Searching UNet with Markov Random Fields

https://github.com/zip-group/spvit

공식 코드

Pruning Self-attentions into Convolutional Layers in Single Path

https://github.com/ziplab/spvit

공식 코드

Pruning Self-attentions into Convolutional Layers in Single Path

https://github.com/zitonghuangcynthia/Simultaneous-Weight-and-Architecture-Optimization

공식 코드

Simultaneous Weight and Architecture Optimization for Neural Networks

https://github.com/zj-zhang/BioNAS-pub

공식 코드

Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge

https://github.com/zj-zhang/elektrum

공식 코드

Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactions

https://github.com/zxcv123456qwe/nisneak

공식 코드

Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health

https://github.com/zxcvfd13502/ddas_code

공식 코드

Direct Differentiable Augmentation Search

https://github.com/zxj32/wr-ssl

공식 코드

How Out-of-Distribution Data Hurts Semi-Supervised Learning

https://gitlab.com/Yvan_Lucas/hmm-ccfd

공식 코드

Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

https://gitlab.com/cioflan/ms-ranas

공식 코드

MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search

https://gitlab.com/computationalscience/near-optimal-control

공식 코드

Near-optimal control of dynamical systems with neural ordinary differential equations

https://gitlab.com/d-towers/nas-reproducibility

공식 코드

Long-term Reproducibility for Neural Architecture Search

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