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 391400 of 641 papers

TitleStatusHype
A Survey on Multi-Objective Neural Architecture Search0
Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction0
AutoADR: Automatic Model Design for Ad Relevance0
AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates0
autoBagging: Learning to Rank Bagging Workflows with Metalearning0
AutoBSS: An Efficient Algorithm for Block Stacking Style Search0
AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance0
AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement0
AutoCompete: A Framework for Machine Learning Competitions0
AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection0
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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