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 126150 of 641 papers

TitleStatusHype
Multi-Objective Evolutionary Design of Composite Data-Driven ModelsCode1
Automated Machine Learning on Graphs: A SurveyCode1
Conditional Positional Encodings for Vision TransformersCode1
CATE: Computation-aware Neural Architecture Encoding with TransformersCode1
Squirrel: A Switching Hyperparameter OptimizerCode1
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality MeasurementsCode1
VEGA: Towards an End-to-End Configurable AutoML PipelineCode1
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label NoiseCode1
Smooth Variational Graph Embeddings for Efficient Neural Architecture SearchCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Automating Outlier Detection via Meta-LearningCode1
Hyperparameter Optimization via Sequential Uniform DesignsCode1
DARTS-: Robustly Stepping out of Performance Collapse Without IndicatorsCode1
AIPerf: Automated machine learning as an AI-HPC benchmarkCode1
Shape Adaptor: A Learnable Resizing ModuleCode1
Loss Function Search for Face RecognitionCode1
GAMA: a General Automated Machine learning AssistantCode1
AutoGAN-Distiller: Searching to Compress Generative Adversarial NetworksCode1
Neural Ensemble Search for Uncertainty Estimation and Dataset ShiftCode1
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?Code1
Efficient AutoML Pipeline Search with Matrix and Tensor FactorizationCode1
AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018Code1
Noisy Differentiable Architecture SearchCode1
DriveML: An R Package for Driverless Machine LearningCode1
Lite Transformer with Long-Short Range AttentionCode1
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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