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 501–550 of 641 papers

TitleStatusHype
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
Efficient Model Adaptation for Continual Learning at the Edge—0
Efficient Multi-stage Inference on Tabular Data—0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications—0
Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring—0
Enhancing Prediction and Analysis of UK Road Traffic Accident Severity Using AI: Integration of Machine Learning, Econometric Techniques, and Time Series Forecasting in Public Health Research—0
Ensemble Squared: A Meta AutoML System—0
eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems—0
Evaluating Generic Auto-ML Tools for Computational Pathology—0
Evaluation of Representation Models for Text Classification with AutoML Tools—0
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System—0
Evolving Machine Learning Algorithms From Scratch—0
Evolving machine learning workflows through interactive AutoML—0
Explainable Automated Machine Learning for Credit Decisions: Enhancing Human Artificial Intelligence Collaboration in Financial Engineering—0
Exploring Visual Complaints through a test battery in Acquired Brain Injury Patients: A Detailed Analysis of the DiaNAH Dataset—0
Extreme AutoML: Analysis of Classification, Regression, and NLP Performance—0
FairAutoML: Embracing Unfairness Mitigation in AutoML—0
Fair AutoML Through Multi-objective Optimization—0
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation—0
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation—0
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures—0
Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop—0
Floralens: a Deep Learning Model for the Portuguese Native Flora—0
FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames—0
Generating Diverse Synthetic Datasets for Evaluation of Real-life Recommender Systems—0
Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation—0
Grad-Instructor: Universal Backpropagation with Explainable Evaluation Neural Networks for Meta-learning and AutoML—0
Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity—0
Graph Pruning for Model Compression—0
Gravitational wave surrogates through automated machine learning—0
Guided Evolution with Binary Discriminators for ML Program Search—0
FEATHERS: Federated Architecture and Hyperparameter Search—0
Hardware-Centric AutoML for Mixed-Precision Quantization—0
High-throughput Cotton Phenotyping Big Data Pipeline Lambda Architecture Computer Vision Deep Neural Networks—0
How Much Automation Does a Data Scientist Want?—0
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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