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 481490 of 641 papers

TitleStatusHype
Transfer Learning with Neural AutoML0
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems0
United States Road Accident Prediction using Random Forest Predictor0
A Versatile Graph Learning Approach through LLM-based Agent0
Using Audio Data to Facilitate Depression Risk Assessment in Primary Health Care0
Using Combinatorial Optimization to Design a High quality LLM Solution0
Using Known Information to Accelerate HyperParameters Optimization Based on SMBO0
Variation in prediction accuracy due to randomness in data division and fair evaluation using interval estimation0
A User-based Visual Analytics Workflow for Exploratory Model Analysis0
Visus: An Interactive System for Automatic Machine Learning Model Building and Curation0
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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