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
10 Years of Fair Representations: Challenges and Opportunities—0
Zero-Touch Networks: Towards Next-Generation Network Automation—0
Approximation capability of neural networks on spaces of probability measures and tree-structured domains—0
CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction—0
Accelerator-aware Neural Network Design using AutoML—0
A CNN toolbox for skin cancer classification—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning—0
Optimal Pricing for Data-Augmented AutoML Marketplaces—0
A Deep Neural Networks ensemble workflow from hyperparameter search to inference leveraging GPU clusters—0
A Framework for the Automated Parameterization of a Sensorless Bearing Fault Detection Pipeline—0
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data—0
A General Recipe for Automated Machine Learning in Practice—0
AI-based Classification of Customer Support Tickets: State of the Art and Implementation with AutoML—0
AutoML to Date and Beyond: Challenges and Opportunities—0
AlphaD3M: Machine Learning Pipeline Synthesis—0
Amazon SageMaker Autopilot: a white box AutoML solution at scale—0
Crop and weed classification based on AutoML—0
AMLA: an AutoML frAmework for Neural Network Design—0
A multi-objective perspective on jointly tuning hardware and hyperparameters—0
Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures—0
Analysis of the AutoML Challenge Series 2015–2018—0
An Approach for Efficient Neural Architecture Search Space Definition—0
An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance—0
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization—0
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