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 2130 of 641 papers

TitleStatusHype
Layered TPOT: Speeding up Tree-based Pipeline OptimizationCode3
Auto-Sklearn 2.0: Hands-free AutoML via Meta-LearningCode3
DeepCAVE: An Interactive Analysis Tool for Automated Machine LearningCode3
Model-based Asynchronous Hyperparameter and Neural Architecture SearchCode3
From Tiny Machine Learning to Tiny Deep Learning: A SurveyCode2
AMC: AutoML for Model Compression and Acceleration on Mobile DevicesCode2
Fraud Dataset Benchmark and ApplicationsCode2
forester: A Tree-Based AutoML Tool in RCode2
AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoMLCode2
GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data AnalysisCode2
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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