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 351360 of 641 papers

TitleStatusHype
Gravitational wave surrogates through automated machine learning0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
On the Security Risks of AutoMLCode0
Human-Centered AI for Data Science: A Systematic Approach0
What can multi-cloud configuration learn from AutoML?0
AutoML to generate ensembles of deep neural networks0
Fair AutoML Through Multi-objective Optimization0
Learning meta-features for AutoMLCode1
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter OptimizationCode2
OMPQ: Orthogonal Mixed Precision QuantizationCode1
Show:102550
← PrevPage 36 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