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 326–350 of 641 papers

TitleStatusHype
Towards AutoML in the presence of Drift: first results—0
Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Prediction—0
Towards Green Automated Machine Learning: Status Quo and Future Directions—0
Towards Human Centered AutoML—0
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks—0
Towards Personalized Preprocessing Pipeline Search—0
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model—0
Transferable AutoML by Model Sharing Over Grouped Datasets—0
Transfer Learning with Neural AutoML—0
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems—0
United States Road Accident Prediction using Random Forest Predictor—0
A Versatile Graph Learning Approach through LLM-based Agent—0
Using Audio Data to Facilitate Depression Risk Assessment in Primary Health Care—0
Using Combinatorial Optimization to Design a High quality LLM Solution—0
Using Known Information to Accelerate HyperParameters Optimization Based on SMBO—0
Variation in prediction accuracy due to randomness in data division and fair evaluation using interval estimation—0
A User-based Visual Analytics Workflow for Exploratory Model Analysis—0
Visus: An Interactive System for Automatic Machine Learning Model Building and Curation—0
Warm-starting DARTS using meta-learning—0
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap—0
What Can AutoML Do For Continual Learning?—0
What can multi-cloud configuration learn from AutoML?—0
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows—0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
ZeroML: A Next Generation AutoML Language—0
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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