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 451460 of 641 papers

TitleStatusHype
Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance0
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality MeasurementsCode1
Demo Application for the AutoGOAL Framework0
Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing0
Long Short Term Memory Networks for Bandwidth Forecasting in Mobile Broadband Networks under Mobility0
Automatic selection of clustering algorithms using supervised graph embeddingCode0
Automated Model Compression by Jointly Applied Pruning and Quantization0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning0
FDNAS: Improving Data Privacy and Model Diversity in AutoML0
VEGA: Towards an End-to-End Configurable AutoML PipelineCode1
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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