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 551560 of 641 papers

TitleStatusHype
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoMLCode0
Learning Activation Functions for Sparse Neural NetworksCode0
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak LearnersCode0
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture SearchCode0
A Robust Experimental Evaluation of Automated Multi-Label Classification MethodsCode0
Automatic and effective discovery of quantum kernelsCode0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
Exploring the Determinants of Pedestrian Crash Severity Using an AutoML ApproachCode0
Exploring Robust Architectures for Deep Artificial Neural NetworksCode0
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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