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A survey of deep meta-learning

The Artificial intelligence review, 2021-08, Vol.54 (6), p.4483-4541

2021

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Metalearning: Applications to Automated Machine Learning and Data Mining

Second edition., 2022

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Fast and Informative Model Selection Using Learning Curve Cross-Validation

IEEE transactions on pattern analysis and machine intelligence, 2023-08, Vol.45 (8), p.9669-9680

2023

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The online performance estimation framework: heterogeneous ensemble learning for data streams

Machine learning, 2018, Vol.107 (1), p.149-176

2018

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The algorithm selection competitions 2015 and 2017

Artificial intelligence, 2019-07, Vol.272, p.86-100

2019

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Multi-task learning with a natural metric for quantitative structure activity relationship learning

Journal of cheminformatics, 2019-11, Vol.11 (1), p.68-13, Article 68

2019

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Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19-20, 2020, Revised Selected Papers

1st ed. 2021., 2021

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Understanding transfer learning and gradient-based meta-learning techniques

Machine learning, 2024-07, Vol.113 (7), p.4113-4132

2024

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Stateless neural meta-learning using second-order gradients

Machine learning, 2022-09, Vol.111 (9), p.3227-3244

2022

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Subspace Adaptation Prior for Few-Shot Learning

Machine learning, 2024-02, Vol.113 (2), p.725-752

2024

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Speeding up algorithm selection using average ranking and active testing by introducing runtime

Machine learning, 2018, Vol.107 (1), p.79-108

2018

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Are LSTMs good few-shot learners?

Machine learning, 2023-11, Vol.112 (11), p.4635-4662

2023

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Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio

Machine learning, 2022-12, Vol.111 (12), p.4565-4584

2022

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Hyperparameter importance and optimization of quantum neural networks across small datasets

Machine learning, 2024-04, Vol.113 (4), p.1941-1966

2024

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The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks

Environmental science & technology, 2023-11, Vol.57 (46), p.17818-17830

2023

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Automated machine learning: past, present and future

The Artificial intelligence review, 2024-04, Vol.57 (5), p.122, Article 122

2024

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Hyperparameter Importance Across Datasets

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, p.2367-2376

2018

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Learning Curve Extrapolation Methods Across Extrapolation Settings

Advances in Intelligent Data Analysis XXII, p.145-157

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Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19–20, 2020, Revised Selected Papers

2021

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Don’t Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML

Advances in Intelligent Data Analysis XVII, p.303-315

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LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks

Machine Learning and Knowledge Discovery in Databases, p.3-19

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Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams

2015 IEEE International Conference on Data Mining, 2015, p.1003-1008

2015

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Comparing Correction Methods to Reduce Misclassification Bias

Artificial Intelligence and Machine Learning, 2021, Vol.1398, p.64-90

2021

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Continuous Surrogate-Based Optimization Algorithms Are Well-Suited for Expensive Discrete Problems

Artificial Intelligence and Machine Learning, 2021, Vol.1398, p.48-63

2021

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Swarm Construction Coordinated Through the Building Material

Artificial Intelligence and Machine Learning, 2021, Vol.1398, p.188-202

2021

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A survey of deep meta-learning

Metalearning: Applications to Automated Machine Learning and Data Mining

Fast and Informative Model Selection Using Learning Curve Cross-Validation

The online performance estimation framework: heterogeneous ensemble learning for data streams

The algorithm selection competitions 2015 and 2017

Multi-task learning with a natural metric for quantitative structure activity relationship learning

Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19-20, 2020, Revised Selected Papers

Understanding transfer learning and gradient-based meta-learning techniques

Stateless neural meta-learning using second-order gradients

Subspace Adaptation Prior for Few-Shot Learning

Speeding up algorithm selection using average ranking and active testing by introducing runtime

Are LSTMs good few-shot learners?

Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio

Hyperparameter importance and optimization of quantum neural networks across small datasets

The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks

Automated machine learning: past, present and future

Hyperparameter Importance Across Datasets

Learning Curve Extrapolation Methods Across Extrapolation Settings

Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19–20, 2020, Revised Selected Papers

Don’t Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML

LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks

Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams

Comparing Correction Methods to Reduce Misclassification Bias

Continuous Surrogate-Based Optimization Algorithms Are Well-Suited for Expensive Discrete Problems

Swarm Construction Coordinated Through the Building Material

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