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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–300 of 641 papers

TitleStatusHype
AutoPINN: When AutoML Meets Physics-Informed Neural Networks—0
AutoPDL: Automatic Prompt Optimization for LLM Agents—0
AutoDOViz: Human-Centered Automation for Decision Optimization—0
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
A General Recipe for Automated Machine Learning in Practice—0
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data—0
Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop—0
AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection—0
An Approach for Efficient Neural Architecture Search Space Definition—0
An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance—0
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
AutoML to generate ensembles of deep neural networks—0
AutoML Systems For Medical Imaging—0
AutoCompete: A Framework for Machine Learning Competitions—0
AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text—0
AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement—0
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures—0
Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation—0
AutoML @ NeurIPS 2018 challenge: Design and Results—0
AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance—0
A Framework for the Automated Parameterization of a Sensorless Bearing Fault Detection Pipeline—0
AutoML in The Wild: Obstacles, Workarounds, and Expectations—0
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks—0
Analysis of the AutoML Challenge Series 2015–2018—0
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All datasetsChalearn-AutoML-1Breast Cancer Coimbra Data SetOrdinalDatasetWine

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