Search Results for author: Jan N. van Rijn

Found 20 papers, 12 papers with code

Subspace Adaptation Prior for Few-Shot Learning

1 code implementation13 Oct 2023 Mike Huisman, Aske Plaat, Jan N. van Rijn

Gradient-based meta-learning techniques aim to distill useful prior knowledge from a set of training tasks such that new tasks can be learned more efficiently with gradient descent.

Few-Shot Image Classification Few-Shot Learning

Understanding Transfer Learning and Gradient-Based Meta-Learning Techniques

1 code implementation9 Oct 2023 Mike Huisman, Aske Plaat, Jan N. van Rijn

Whilst meta-learning techniques have been observed to be successful at this in various scenarios, recent results suggest that when evaluated on tasks from a different data distribution than the one used for training, a baseline that simply finetunes a pre-trained network may be more effective than more complicated meta-learning techniques such as MAML, which is one of the most popular meta-learning techniques.

Meta-Learning Transfer Learning

Hyperparameter Importance of Quantum Neural Networks Across Small Datasets

no code implementations20 Jun 2022 Charles Moussa, Jan N. van Rijn, Thomas Bäck, Vedran Dunjko

In this domain, one of the more investigated approaches is the use of a special type of quantum circuit - a so-called quantum neural network -- to serve as a basis for a machine learning model.

BIG-bench Machine Learning Model Selection +1

Learning Curves for Decision Making in Supervised Machine Learning -- A Survey

no code implementations28 Jan 2022 Felix Mohr, Jan N. van Rijn

Learning curves are a concept from social sciences that has been adopted in the context of machine learning to assess the performance of a learning algorithm with respect to a certain resource, e. g. the number of training examples or the number of training iterations.

BIG-bench Machine Learning Decision Making +1

Fast and Informative Model Selection using Learning Curve Cross-Validation

1 code implementation27 Nov 2021 Felix Mohr, Jan N. van Rijn

Common cross-validation (CV) methods like k-fold cross-validation or Monte-Carlo cross-validation estimate the predictive performance of a learner by repeatedly training it on a large portion of the given data and testing on the remaining data.

AutoML Model Selection

Meta-Learning for Symbolic Hyperparameter Defaults

1 code implementation10 Jun 2021 Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren

Hyperparameter optimization in machine learning (ML) deals with the problem of empirically learning an optimal algorithm configuration from data, usually formulated as a black-box optimization problem.

Hyperparameter Optimization Meta-Learning

Towards Model Selection using Learning Curve Cross-Validation

1 code implementation ICML Workshop AutoML 2021 Felix Mohr, Jan N. van Rijn

We run a large scale experiment on the 67 datasets from the AutoML benchmark, and empirically show that LCCV in over 90\% of the cases leads to similar performance (at most 0. 5\% difference) as 10-fold CV, but provides additional insights on the behaviour of a given model.

AutoML Model Selection

Stateless Neural Meta-Learning using Second-Order Gradients

1 code implementation21 Apr 2021 Mike Huisman, Aske Plaat, Jan N. van Rijn

Deep learning typically requires large data sets and much compute power for each new problem that is learned.

Image Classification Meta-Learning

A Survey of Deep Meta-Learning

no code implementations7 Oct 2020 Mike Huisman, Jan N. van Rijn, Aske Plaat

Meta-learning is one approach to address this issue, by enabling the network to learn how to learn.

Meta-Learning

OpenML-Python: an extensible Python API for OpenML

1 code implementation6 Nov 2019 Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter

It also provides functionality to conduct machine learning experiments, upload the results to OpenML, and reproduce results which are stored on OpenML.

BIG-bench Machine Learning

The Algorithm Selection Competitions 2015 and 2017

no code implementations3 May 2018 Marius Lindauer, Jan N. van Rijn, Lars Kotthoff

The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance.

The Complexity of Rummikub Problems

1 code implementation26 Apr 2016 Jan N. van Rijn, Frank W. Takes, Jonathan K. Vis

Rummikub is a tile-based game in which each player starts with a hand of $14$ tiles.

Computational Complexity

Endgame Analysis of Dou Shou Qi

no code implementations25 Apr 2016 Jan N. van Rijn, Jonathan K. Vis

Dou Shou Qi is a game in which two players control a number of pieces, each of them aiming to move one of their pieces onto a given square.

OpenML: networked science in machine learning

1 code implementation29 Jul 2014 Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luis Torgo

Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals.

BIG-bench Machine Learning

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