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

Showing 131140 of 641 papers

TitleStatusHype
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality MeasurementsCode1
VEGA: Towards an End-to-End Configurable AutoML PipelineCode1
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label NoiseCode1
Smooth Variational Graph Embeddings for Efficient Neural Architecture SearchCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Automating Outlier Detection via Meta-LearningCode1
Hyperparameter Optimization via Sequential Uniform DesignsCode1
DARTS-: Robustly Stepping out of Performance Collapse Without IndicatorsCode1
AIPerf: Automated machine learning as an AI-HPC benchmarkCode1
Shape Adaptor: A Learnable Resizing ModuleCode1
Show:102550
← PrevPage 14 of 65Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4Unverified
2reference_mbRank (AutoML5)5.2Unverified
3postech.mlg_exbrainRank (AutoML5)5.2Unverified
4abhishek4Rank (AutoML5)4.6Unverified
5referenceRank (AutoML5)4.4Unverified
6reference_lsRank (AutoML5)4Unverified
7djajeticRank (AutoML5)3Unverified
8aad_freiburgRank (AutoML5)1.6Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33Unverified