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
An AutoML-based approach for Network Intrusion Detection—0
An AutoML-based Approach to Multimodal Image Sentiment Analysis—0
A New Deep Neural Architecture Search Pipeline for Face Recognition—0
An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization—0
An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)—0
AnnotatedTables: A Large Tabular Dataset with Language Model Annotations—0
An Open Source AutoML Benchmark—0
Approximation capability of neural networks on sets of probability measures and tree-structured data—0
ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages—0
Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation—0
Are Large Language Models the New Interface for Data Pipelines?—0
Exploring the Intersection between Neural Architecture Search and Continual Learning—0
A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases—0
ASP: Automatic Selection of Proxy dataset for efficient AutoML—0
Assessing the Use of AutoML for Data-Driven Software Engineering—0
A Survey on Multi-Objective Neural Architecture Search—0
Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction—0
AutoADR: Automatic Model Design for Ad Relevance—0
AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates—0
autoBagging: Learning to Rank Bagging Workflows with Metalearning—0
AutoBSS: An Efficient Algorithm for Block Stacking Style Search—0
AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance—0
AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement—0
AutoCompete: A Framework for Machine Learning Competitions—0
AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection—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