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 351–375 of 641 papers

TitleStatusHype
Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics—0
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods—0
Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models—0
Data Readiness Report—0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making—0
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications—0
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering—0
Demo Application for the AutoGOAL Framework—0
Democratize with Care: The need for fairness specific features in user-interface based open source AutoML tools—0
Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization—0
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture—0
Diagnosis of sickle cell anemia using AutoML on UV-Vis absorbance spectroscopy data—0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
DiffraNet: Automatic Classification of Serial Crystallography Diffraction Patterns—0
Discovering Adaptable Symbolic Algorithms from Scratch—0
DIVA: Dataset Derivative of a Learning Task—0
DivBO: Diversity-aware CASH for Ensemble Learning—0
DREAM: Debugging and Repairing AutoML Pipelines—0
E8-IJS@LT-EDI-ACL2022 - BERT, AutoML and Knowledge-graph backed Detection of Depression—0
ECONOMIC HYPERPARAMETER OPTIMIZATION WITH BLENDED SEARCH STRATEGY—0
Efficient Automatic CASH via Rising Bandits—0
Efficient Data-specific Model Search for Collaborative Filtering—0
Learning to Be A Doctor: Searching for Effective Medical Agent Architectures—0
Lessons learned from the AutoML challenge—0
Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance—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