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 231240 of 641 papers

TitleStatusHype
AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architecturesCode0
AutoML in The Wild: Obstacles, Workarounds, and Expectations0
AutoDOViz: Human-Centered Automation for Decision Optimization0
Cross-Modal Fine-Tuning: Align then RefineCode1
Unified Functional Hashing in Automatic Machine LearningCode1
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning PipelinesCode0
DivBO: Diversity-aware CASH for Ensemble Learning0
Open Problems in Applied Deep LearningCode4
RF+clust for Leave-One-Problem-Out Performance Prediction0
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous EnvironmentCode0
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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