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 401–425 of 641 papers

TitleStatusHype
Privacy-preserving Online AutoML for Domain-Specific Face Detection—0
Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data—0
Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring—0
AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance—0
Mining Robust Default Configurations for Resource-constrained AutoML—0
SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions—0
Review of automated time series forecasting pipelines—0
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy—0
FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames—0
Online AutoML: An adaptive AutoML framework for online learning—0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems—0
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model—0
Neural Architecture Search for Inversion—0
AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection—0
MLOps -- Definitions, Tools and Challenges—0
Evaluating Generic Auto-ML Tools for Computational Pathology—0
Manas: Mining Software Repositories to Assist AutoMLCode0
Pipeline Combinators for Gradual AutoML—0
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
Diagnosis of sickle cell anemia using AutoML on UV-Vis absorbance spectroscopy data—0
DIVA: Dataset Derivative of a Learning Task—0
T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging—0
FairAutoML: Embracing Unfairness Mitigation in AutoML—0
Towards Green Automated Machine Learning: Status Quo and Future Directions—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