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

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Showing 351–400 of 641 papers

TitleStatusHype
Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics—0
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods—0
Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models—0
Data Readiness Report—0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making—0
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications—0
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering—0
Demo Application for the AutoGOAL Framework—0
Democratize with Care: The need for fairness specific features in user-interface based open source AutoML tools—0
Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization—0
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture—0
Diagnosis of sickle cell anemia using AutoML on UV-Vis absorbance spectroscopy data—0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
DiffraNet: Automatic Classification of Serial Crystallography Diffraction Patterns—0
Discovering Adaptable Symbolic Algorithms from Scratch—0
DIVA: Dataset Derivative of a Learning Task—0
DivBO: Diversity-aware CASH for Ensemble Learning—0
DREAM: Debugging and Repairing AutoML Pipelines—0
E8-IJS@LT-EDI-ACL2022 - BERT, AutoML and Knowledge-graph backed Detection of Depression—0
ECONOMIC HYPERPARAMETER OPTIMIZATION WITH BLENDED SEARCH STRATEGY—0
Efficient Automatic CASH via Rising Bandits—0
Efficient Data-specific Model Search for Collaborative Filtering—0
Learning to Be A Doctor: Searching for Effective Medical Agent Architectures—0
Lessons learned from the AutoML challenge—0
Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance—0
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection—0
Long Short Term Memory Networks for Bandwidth Forecasting in Mobile Broadband Networks under Mobility—0
MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts—0
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
Man versus Machine: AutoML and Human Experts' Role in Phishing Detection—0
Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques—0
MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat—0
M-DEW: Extending Dynamic Ensemble Weighting to Handle Missing Values—0
Melatect: A Machine Learning Model Approach For Identifying Malignant Melanoma in Skin Growths—0
Memory-efficient Embedding for Recommendations—0
Mental Disorders Detection in the Era of Large Language Models—0
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round—0
Meta-Learning from Learning Curves for Budget-Limited Algorithm Selection—0
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications—0
Metalearning Using Structure-rich Pipeline Representations for Better AutoML—0
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning—0
Mining Robust Default Configurations for Resource-constrained AutoML—0
MLOps -- Definitions, Tools and Challenges—0
Model Evaluation for Domain Identification of Unknown Classes in Open-World Recognition: A Proposal—0
Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data—0
Modeling All Response Surfaces in One for Conditional Search Spaces—0
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML—0
MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders—0
Multi-Agent Automated Machine Learning—0
Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm—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