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 4150 of 641 papers

TitleStatusHype
Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation0
Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data0
Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals0
Extreme AutoML: Analysis of Classification, Regression, and NLP Performance0
Generating Diverse Synthetic Datasets for Evaluation of Real-life Recommender Systems0
An AutoML-based approach for Network Intrusion Detection0
Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanism0
Creation and Evaluation of a Food Product Image Dataset for Product Property Extraction0
Towards Automated Model Design on Recommender SystemsCode1
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A SurveyCode0
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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