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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 601–625 of 641 papers

Title	Date	Tasks	Status	Hype
Continual Learning in Practice	Mar 12, 2019	AutoMLBIG-bench Machine Learning	—Unverified	0
AutoML @ NeurIPS 2018 challenge: Design and Results	Mar 12, 2019	AutoMLBIG-bench Machine Learning	—Unverified	0
SAFE ML: Surrogate Assisted Feature Extraction for Model Learning	Feb 28, 2019	AutoMLFeature Engineering	CodeCode Available	0
Evolutionary Neural AutoML for Deep Learning	Feb 18, 2019	AutoMLDeep Learning	CodeCode Available	1
AutoQ: Automated Kernel-Wise Neural Network Quantization	Feb 15, 2019	AutoMLDeep Reinforcement Learning	—Unverified	0
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning	Feb 13, 2019	AutoMLBIG-bench Machine Learning	CodeCode Available	0
Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural Networks	Jan 24, 2019	AutoMLClassification	CodeCode Available	0
Katib: A Distributed General AutoML Platform on Kubernetes	Jan 1, 2019	AutoMLHyperparameter Optimization	—Unverified	0
DarwinML: A Graph-based Evolutionary Algorithm for Automated Machine Learning	Nov 20, 2018	AutoMLBIG-bench Machine Learning	—Unverified	0
Automated Multi-Label Classification based on ML-Plan	Nov 9, 2018	AutoMLBIG-bench Machine Learning	—Unverified	0
A Very Brief and Critical Discussion on AutoML	Nov 9, 2018	AutoML	—Unverified	0
Using Known Information to Accelerate HyperParameters Optimization Based on SMBO	Nov 8, 2018	AutoMLBIG-bench Machine Learning	—Unverified	0
Auto-ML Deep Learning for Rashi Scripts OCR	Nov 3, 2018	AutoMLDeep Learning	—Unverified	0
Automated Machine Learning: From Principles to Practices	Oct 31, 2018	AutoMLBIG-bench Machine Learning	CodeCode Available	0
DiffraNet: Automatic Classification of Serial Crystallography Diffraction Patterns	Sep 27, 2018	AutoMLClassification	—Unverified	0
A User-based Visual Analytics Workflow for Exploratory Model Analysis	Sep 27, 2018	AutoMLModel Selection	—Unverified	0
Benchmarking Automatic Machine Learning Frameworks	Aug 17, 2018	Automated Feature EngineeringAutoML	CodeCode Available	3
OBOE: Collaborative Filtering for AutoML Model Selection	Aug 9, 2018	Active LearningAutoML	CodeCode Available	1
Automatic Gradient Boosting	Jul 10, 2018	AutoMLBIG-bench Machine Learning	CodeCode Available	0
ML-Plan: Automated machine learning via hierarchical planning	Jul 3, 2018	AutoMLBIG-bench Machine Learning	CodeCode Available	0
Auto-Keras: An Efficient Neural Architecture Search System	Jun 27, 2018	AutoMLBayesian Optimization	CodeCode Available	0
Lessons learned from the AutoML challenge	Jun 20, 2018	AutoMLregression	—Unverified	0
Rafiki: Machine Learning as an Analytics Service System	Apr 17, 2018	AutoMLBIG-bench Machine Learning	CodeCode Available	0
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI	Mar 17, 2018	AutoMLModel Selection	—Unverified	0
Transfer Learning with Neural AutoML	Mar 7, 2018	AutoMLGeneral Classification	—Unverified	0

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All datasets Chalearn-AutoML-1 Breast Cancer Coimbra Data Set OrdinalDataset Wine

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	marc.boulle	Rank (AutoML5)	6.4	—	Unverified
2	reference_mb	Rank (AutoML5)	5.2	—	Unverified
3	postech.mlg_exbrain	Rank (AutoML5)	5.2	—	Unverified
4	abhishek4	Rank (AutoML5)	4.6	—	Unverified
5	reference	Rank (AutoML5)	4.4	—	Unverified
6	reference_ls	Rank (AutoML5)	4	—	Unverified
7	djajetic	Rank (AutoML5)	3	—	Unverified
8	aad_freiburg	Rank (AutoML5)	1.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Logistic Regression	Accuracy	97.02	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Zero-shot-BERT-SORT	1:1 Accuracy	55	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Logistic Regression	accuracy	98.33	—	Unverified