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
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems—0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model—0
Neural Architecture Search for Inversion—0
MLOps -- Definitions, Tools and Challenges—0
AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection—0
Transformers Can Do Bayesian InferenceCode1
Evaluating Generic Auto-ML Tools for Computational Pathology—0
Manas: Mining Software Repositories to Assist AutoMLCode0
Pipeline Combinators for Gradual AutoML—0
Naive Automated Machine LearningCode1
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
Diagnosis of sickle cell anemia using AutoML on UV-Vis absorbance spectroscopy data—0
AutoDC: Automated data-centric processingCode1
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
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search—0
NAS-Bench-x11 and the Power of Learning CurvesCode1
Benchmarking Multimodal AutoML for Tabular Data with Text FieldsCode3
AlphaD3M: Machine Learning Pipeline Synthesis—0
A Scalable AutoML Approach Based on Graph Neural NetworksCode0
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationCode2
On Predictive Explanation of Data Anomalies—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