SOTAVerified

AutoML

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

Papers

Showing 171180 of 641 papers

TitleStatusHype
Exploring the Intersection between Neural Architecture Search and Continual Learning0
Automated Multi-Label Classification based on ML-Plan0
Automated Model Compression by Jointly Applied Pruning and Quantization0
Are Large Language Models the New Interface for Data Pipelines?0
ChatGPT as your Personal Data Scientist0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure0
An ADMM Based Framework for AutoML Pipeline Configuration0
Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation0
Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs0
Automated Machine Learning in Practice: State of the Art and Recent Results0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4Unverified
2reference_mbRank (AutoML5)5.2Unverified
3postech.mlg_exbrainRank (AutoML5)5.2Unverified
4abhishek4Rank (AutoML5)4.6Unverified
5referenceRank (AutoML5)4.4Unverified
6reference_lsRank (AutoML5)4Unverified
7djajeticRank (AutoML5)3Unverified
8aad_freiburgRank (AutoML5)1.6Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33Unverified