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
Gravitational wave surrogates through automated machine learning—0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
On the Security Risks of AutoMLCode0
Human-Centered AI for Data Science: A Systematic Approach—0
What can multi-cloud configuration learn from AutoML?—0
AutoML to generate ensembles of deep neural networks—0
Fair AutoML Through Multi-objective Optimization—0
Learning meta-features for AutoMLCode1
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter OptimizationCode2
OMPQ: Orthogonal Mixed Precision QuantizationCode1
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOCode1
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning—0
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture—0
Automatic Componentwise Boosting: An Interpretable AutoML System—0
Automated Machine Learning, Bounded Rationality, and Rational Metareasoning—0
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational DataCode1
Melatect: A Machine Learning Model Approach For Identifying Malignant Melanoma in Skin Growths—0
LightAutoML: AutoML Solution for a Large Financial Services EcosystemCode2
Selecting Optimal Trace Clustering Pipelines with AutoML—0
Communication-Computation Efficient Device-Edge Co-Inference via AutoML—0
Man versus Machine: AutoML and Human Experts' Role in Phishing Detection—0
An Information Theory-inspired Strategy for Automatic Network PruningCode1
AutoVideo: An Automated Video Action Recognition SystemCode1
AMDet: A Tool for Mitotic Cell Detection in Histopathology SlidesCode0
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries ChallengeCode0
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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