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 311320 of 641 papers

TitleStatusHype
COPA: Comparing the Incomparable to Explore the Pareto Front0
An ADMM Based Framework for AutoML Pipeline Configuration0
Continual Learning in Practice0
Constructing a meta-learner for unsupervised anomaly detection0
Considerations of automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure0
Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation0
Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification0
Complex Mixer for MedMNIST Classification Decathlon0
Automated Machine Learning in Practice: State of the Art and Recent Results0
Comparing AutoML and Deep Learning Methods for Condition Monitoring using Realistic Validation Scenarios0
Show:102550
← PrevPage 32 of 65Next →

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