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 376–400 of 641 papers

TitleStatusHype
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