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 221230 of 641 papers

TitleStatusHype
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoMLCode0
AutoML-Zero: Evolving Machine Learning Algorithms From ScratchCode0
Exploring the Determinants of Pedestrian Crash Severity Using an AutoML ApproachCode0
AutoML using Metadata Language EmbeddingsCode0
Evolutionary Architecture Search for Graph Neural NetworksCode0
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical EvolutionCode0
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data ScienceCode0
Exploring Robust Architectures for Deep Artificial Neural NetworksCode0
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture SearchCode0
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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