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 251–275 of 641 papers

TitleStatusHype
Evolving machine learning workflows through interactive AutoML—0
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation—0
A Very Brief and Critical Discussion on AutoML—0
10 Years of Fair Representations: Challenges and Opportunities—0
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models—0
A New Deep Neural Architecture Search Pipeline for Face Recognition—0
eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems—0
Auto-survey Challenge—0
Autostacker: an Automatic Evolutionary Hierarchical Machine Learning System—0
Autostacker: A Compositional Evolutionary Learning System—0
AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification—0
Evaluating Generic Auto-ML Tools for Computational Pathology—0
An AutoML-based Approach to Multimodal Image Sentiment Analysis—0
AI-based Classification of Customer Support Tickets: State of the Art and Implementation with AutoML—0
Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring—0
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
Enhancing Prediction and Analysis of UK Road Traffic Accident Severity Using AI: Integration of Machine Learning, Econometric Techniques, and Time Series Forecasting in Public Health Research—0
Ensemble Squared: A Meta AutoML System—0
Evaluation of Representation Models for Text Classification with AutoML Tools—0
AutoQ: Automated Kernel-Wise Neural Network Quantization—0
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations—0
AutoPruning for Deep Neural Network with Dynamic Channel Masking—0
AutoDS: Towards Human-Centered Automation of Data Science—0
An AutoML-based approach for Network Intrusion Detection—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