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 7180 of 641 papers

TitleStatusHype
DC-BENCH: Dataset Condensation BenchmarkCode1
AIPerf: Automated machine learning as an AI-HPC benchmarkCode1
Deep AutoAugmentCode1
DARTS-: Robustly Stepping out of Performance Collapse Without IndicatorsCode1
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free GrammarsCode1
CliMB: An AI-enabled Partner for Clinical Predictive ModelingCode1
CATE: Computation-aware Neural Architecture Encoding with TransformersCode1
AutoGAN-Distiller: Searching to Compress Generative Adversarial NetworksCode1
Cross-Modal Fine-Tuning: Align then RefineCode1
Deep Fast Vision: Accelerated Deep Transfer Learning Vision Prototyping and BeyondCode1
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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