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 521530 of 641 papers

TitleStatusHype
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical EvolutionCode0
Architecture Disentanglement for Deep Neural NetworksCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep LearningCode1
Model-based Asynchronous Hyperparameter and Neural Architecture SearchCode3
AutoGluon-Tabular: Robust and Accurate AutoML for Structured DataCode3
AutoML-Zero: Evolving Machine Learning Algorithms From ScratchCode0
Accelerator-aware Neural Network Design using AutoML0
BUSU-Net: An Ensemble U-Net Framework for Medical Image Segmentation0
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning0
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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