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 1120 of 641 papers

TitleStatusHype
Auto-nnU-Net: Towards Automated Medical Image SegmentationCode0
MLZero: A Multi-Agent System for End-to-end Machine Learning AutomationCode3
SEAL: Searching Expandable Architectures for Incremental Learning0
Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning0
When Your Own Output Becomes Your Training Data: Noise-to-Meaning Loops and a Formal RSI TriggerCode0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
United States Road Accident Prediction using Random Forest Predictor0
CAPO: Cost-Aware Prompt OptimizationCode2
Learning to Be A Doctor: Searching for Effective Medical Agent Architectures0
LEMUR Neural Network Dataset: Towards Seamless AutoMLCode1
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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