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 511520 of 641 papers

TitleStatusHype
AutoHAS: Efficient Hyperparameter and Architecture Search0
AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018Code1
A Robust Experimental Evaluation of Automated Multi-Label Classification MethodsCode0
A New Deep Neural Architecture Search Pipeline for Face Recognition0
Noisy Differentiable Architecture SearchCode1
Testing the Robustness of AutoML Systems0
Frugal Optimization for Cost-related HyperparametersCode2
DriveML: An R Package for Driverless Machine LearningCode1
Lite Transformer with Long-Short Range AttentionCode1
MetaPoison: Practical General-purpose Clean-label Data PoisoningCode1
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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