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 226–250 of 641 papers

TitleStatusHype
AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural NetworksCode0
KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data ScienceCode1
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System—0
Towards Personalized Preprocessing Pipeline Search—0
Scalable End-to-End ML Platforms: from AutoML to Self-serve—0
AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architecturesCode0
AutoML in The Wild: Obstacles, Workarounds, and Expectations—0
AutoDOViz: Human-Centered Automation for Decision Optimization—0
Cross-Modal Fine-Tuning: Align then RefineCode1
Unified Functional Hashing in Automatic Machine LearningCode1
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning PipelinesCode0
DivBO: Diversity-aware CASH for Ensemble Learning—0
Open Problems in Applied Deep LearningCode4
RF+clust for Leave-One-Problem-Out Performance Prediction—0
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous EnvironmentCode0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen EstimatorCode1
Examining marginal properness in the external validation of survival models with squared and logarithmic lossesCode0
AutoPINN: When AutoML Meets Physics-Informed Neural Networks—0
Benchmarking AutoML algorithms on a collection of synthetic classification problemsCode0
Towards Automated Design of Bayesian Optimization via Exploratory Landscape AnalysisCode0
Hyperparameter optimization in deep multi-target predictionCode1
The Technological Emergence of AutoML: A Survey of Performant Software and Applications in the Context of Industry—0
AutoML-based Almond Yield Prediction and Projection in California—0
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free GrammarsCode1
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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