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 221–230 of 641 papers

TitleStatusHype
A Comprehensive Survey on Automated Machine Learning for Recommendations—0
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
Democratize with Care: The need for fairness specific features in user-interface based open source AutoML tools—0
Batch Bayesian Optimization for Replicable Experimental Design—0
Communication-Computation Efficient Device-Edge Co-Inference via AutoML—0
Comparing AutoML and Deep Learning Methods for Condition Monitoring using Realistic Validation Scenarios—0
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration—0
Complex Mixer for MedMNIST Classification Decathlon—0
Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification—0
AutoHAS: Efficient Hyperparameter and Architecture Search—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4—Unverified
2reference_mbRank (AutoML5)5.2—Unverified
3postech.mlg_exbrainRank (AutoML5)5.2—Unverified
4abhishek4Rank (AutoML5)4.6—Unverified
5referenceRank (AutoML5)4.4—Unverified
6reference_lsRank (AutoML5)4—Unverified
7djajeticRank (AutoML5)3—Unverified
8aad_freiburgRank (AutoML5)1.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02—Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55—Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33—Unverified