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 4150 of 641 papers

TitleStatusHype
DeepMol: An Automated Machine and Deep Learning Framework for Computational ChemistrCode2
HyperFast: Instant Classification for Tabular DataCode2
AutoFormer: Searching Transformers for Visual RecognitionCode2
Frugal Optimization for Cost-related HyperparametersCode2
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDLCode2
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image AnalysisCode2
From Tiny Machine Learning to Tiny Deep Learning: A SurveyCode2
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement LearningCode1
CATE: Computation-aware Neural Architecture Encoding with TransformersCode1
Show:102550
← PrevPage 5 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