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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–475 of 641 papers

TitleStatusHype
Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance—0
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality MeasurementsCode1
Demo Application for the AutoGOAL Framework—0
Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing—0
Long Short Term Memory Networks for Bandwidth Forecasting in Mobile Broadband Networks under Mobility—0
Automatic selection of clustering algorithms using supervised graph embeddingCode0
Automated Model Compression by Jointly Applied Pruning and Quantization—0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning—0
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
VEGA: Towards an End-to-End Configurable AutoML PipelineCode1
Resource-Aware Pareto-Optimal Automated Machine Learning Platform—0
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data—0
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image AnalysisCode2
Crop and weed classification based on AutoML—0
AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification—0
AutoPruning for Deep Neural Network with Dynamic Channel Masking—0
AutoML to Date and Beyond: Challenges and Opportunities—0
AutoBSS: An Efficient Algorithm for Block Stacking Style Search—0
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label NoiseCode1
AutoADR: Automatic Model Design for Ad Relevance—0
Data Readiness Report—0
Revisiting Neural Architecture Search—0
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
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All datasetsChalearn-AutoML-1Breast Cancer Coimbra Data SetOrdinalDatasetWine

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