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 341350 of 641 papers

TitleStatusHype
Variation in prediction accuracy due to randomness in data division and fair evaluation using interval estimation0
A User-based Visual Analytics Workflow for Exploratory Model Analysis0
Visus: An Interactive System for Automatic Machine Learning Model Building and Curation0
Warm-starting DARTS using meta-learning0
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap0
What Can AutoML Do For Continual Learning?0
What can multi-cloud configuration learn from AutoML?0
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 20190
ZeroML: A Next Generation AutoML Language0
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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