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

Showing 601–641 of 641 papers

TitleStatusHype
Continual Learning in Practice—0
AutoML @ NeurIPS 2018 challenge: Design and Results—0
SAFE ML: Surrogate Assisted Feature Extraction for Model LearningCode0
Evolutionary Neural AutoML for Deep LearningCode1
AutoQ: Automated Kernel-Wise Neural Network Quantization—0
ATMSeer: Increasing Transparency and Controllability in Automated Machine LearningCode0
Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural NetworksCode0
Katib: A Distributed General AutoML Platform on Kubernetes—0
DarwinML: A Graph-based Evolutionary Algorithm for Automated Machine Learning—0
Automated Multi-Label Classification based on ML-Plan—0
A Very Brief and Critical Discussion on AutoML—0
Using Known Information to Accelerate HyperParameters Optimization Based on SMBO—0
Auto-ML Deep Learning for Rashi Scripts OCR—0
Automated Machine Learning: From Principles to PracticesCode0
DiffraNet: Automatic Classification of Serial Crystallography Diffraction Patterns—0
A User-based Visual Analytics Workflow for Exploratory Model Analysis—0
Benchmarking Automatic Machine Learning FrameworksCode3
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
Automatic Gradient BoostingCode0
ML-Plan: Automated machine learning via hierarchical planningCode0
Auto-Keras: An Efficient Neural Architecture Search SystemCode0
Lessons learned from the AutoML challenge—0
Rafiki: Machine Learning as an Analytics Service SystemCode0
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI—0
Transfer Learning with Neural AutoML—0
Autostacker: A Compositional Evolutionary Learning System—0
AMC: AutoML for Model Compression and Acceleration on Mobile DevicesCode2
Layered TPOT: Speeding up Tree-based Pipeline OptimizationCode3
AMLA: an AutoML frAmework for Neural Network Design—0
Autostacker: an Automatic Evolutionary Hierarchical Machine Learning System—0
ATM: A distributed, collaborative, scalable system for automated machine learningCode0
Considerations of automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure—0
autoBagging: Learning to Rank Bagging Workflows with Metalearning—0
Google Vizier: A Service for Black-Box OptimizationCode0
Automatic Frankensteining: Creating Complex Ensembles Autonomously—0
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine LearningCode0
Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation ToolCode0
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data ScienceCode0
Efficient and Robust Automated Machine LearningCode3
AutoCompete: A Framework for Machine Learning Competitions—0
Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-LearnCode0
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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