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 621630 of 641 papers

TitleStatusHype
Automated Machine Learning with Monte-Carlo Tree SearchCode0
A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoMLCode0
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI ResearchersCode0
Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural NetworksCode0
Towards Automated Design of Bayesian Optimization via Exploratory Landscape AnalysisCode0
Neural Architecture Search for Sentence Classification with BERTCode0
Auto-nnU-Net: Towards Automated Medical Image SegmentationCode0
Neuraxle - A Python Framework for Neat Machine Learning PipelinesCode0
AutoML-Zero: Evolving Machine Learning Algorithms From ScratchCode0
AutoML using Metadata Language EmbeddingsCode0
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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