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 101110 of 641 papers

TitleStatusHype
Deep AutoAugmentCode1
XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine LearningCode1
Transformers Can Do Bayesian InferenceCode1
Naive Automated Machine LearningCode1
AutoDC: Automated data-centric processingCode1
NAS-Bench-x11 and the Power of Learning CurvesCode1
Learning meta-features for AutoMLCode1
OMPQ: Orthogonal Mixed Precision QuantizationCode1
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOCode1
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational DataCode1
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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