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 11–20 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 Learning—0
Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning—0
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 Predictor—0
CAPO: Cost-Aware Prompt OptimizationCode2
Learning to Be A Doctor: Searching for Effective Medical Agent Architectures—0
LEMUR Neural Network Dataset: Towards Seamless AutoMLCode1
Show:102550
← PrevPage 2 of 65Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4—Unverified
2reference_mbRank (AutoML5)5.2—Unverified
3postech.mlg_exbrainRank (AutoML5)5.2—Unverified
4abhishek4Rank (AutoML5)4.6—Unverified
5referenceRank (AutoML5)4.4—Unverified
6reference_lsRank (AutoML5)4—Unverified
7djajeticRank (AutoML5)3—Unverified
8aad_freiburgRank (AutoML5)1.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02—Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55—Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33—Unverified