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 1120 of 641 papers

TitleStatusHype
Layered TPOT: Speeding up Tree-based Pipeline OptimizationCode3
DeepCAVE: An Interactive Analysis Tool for Automated Machine LearningCode3
MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile DevicesCode3
AutoGluon-Tabular: Robust and Accurate AutoML for Structured DataCode3
Benchmarking Multimodal AutoML for Tabular Data with Text FieldsCode3
Benchmarking Automatic Machine Learning FrameworksCode3
EfficientNetV2: Smaller Models and Faster TrainingCode3
AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative InvestmentCode3
Auto-Sklearn 2.0: Hands-free AutoML via Meta-LearningCode3
Efficient and Robust Automated Machine LearningCode3
Show:102550
← PrevPage 2 of 65Next →

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