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 251260 of 641 papers

TitleStatusHype
Automatic deep learning for trend prediction in time series data0
Extreme AutoML: Analysis of Classification, Regression, and NLP Performance0
A Very Brief and Critical Discussion on AutoML0
10 Years of Fair Representations: Challenges and Opportunities0
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models0
A New Deep Neural Architecture Search Pipeline for Face Recognition0
Efficient Data-specific Model Search for Collaborative Filtering0
Auto-survey Challenge0
Autostacker: an Automatic Evolutionary Hierarchical Machine Learning System0
Autostacker: A Compositional Evolutionary Learning System0
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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