The Shape of Learning Curves: a Review

Tom Viering, Marco Loog

arxiv 2021

code

A Brief Prehistory of Double Descent

Marco Loog, Tom Viering, Alexander Mey, Jesse H. Krijthe, David M.J. Tax

PNAS 2020

Making Learners (More) Monotone

Tom Viering, Alexander Mey, Marco Loog

IDA 2020

code slides video

A Brief Prehistory of Double Descent

Marco Loog, Tom Viering, Alexander Mey, Jesse H. Krijthe, David M.J. Tax

arxiv preprint 2020

A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization

Alexander Mey, Tom Julian Viering, Marco Loog

IDA 2020

Making Learners (More) Monotone

Tom Viering, Alexander Mey, Marco Loog

arxiv preprint 2019

How to Manipulate CNNs to Make Them Lie: the GradCAM Case

Tom Viering, Ziqi Wang, Marco Loog, Elmar Eisemann

arxiv preprint 2019

slides

Minimizers of the Empirical Risk and Risk Monotonicity

Marco Loog, Tom Viering, Alexander Mey

in NeurIPS 2019

code slides poster

A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization

A Mey, T Viering, M Loog

arxiv preprint 2019

Nuclear discrepancy for single-shot batch active learning

Tom J Viering, Jesse H Krijthe, Marco Loog

in Machine Learning 2019

code slides poster

Open Problem: Monotonicity of Learning

T Viering, A Mey, M Loog

in COLT 2019

Nuclear Discrepancy for Active Learning

Tom J Viering, Jesse H Krijthe, Marco Loog

arxiv preprint 2017

Generalization Bound Minimization for Active Learning

Tom J Viering, Jesse H Krijthe, Marco Loog

in Benelearn 2017

Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds

T J Viering

masterthesis 2016

Tom Viering, Marco Loog

arxiv 2021

code

A Brief Prehistory of Double Descent

Marco Loog, Tom Viering, Alexander Mey, Jesse H. Krijthe, David M.J. Tax

PNAS 2020

Making Learners (More) Monotone

Tom Viering, Alexander Mey, Marco Loog

IDA 2020

code slides video

A Brief Prehistory of Double Descent

Marco Loog, Tom Viering, Alexander Mey, Jesse H. Krijthe, David M.J. Tax

arxiv preprint 2020

A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization

Alexander Mey, Tom Julian Viering, Marco Loog

IDA 2020

Making Learners (More) Monotone

Tom Viering, Alexander Mey, Marco Loog

arxiv preprint 2019

How to Manipulate CNNs to Make Them Lie: the GradCAM Case

Tom Viering, Ziqi Wang, Marco Loog, Elmar Eisemann

arxiv preprint 2019

slides

Minimizers of the Empirical Risk and Risk Monotonicity

Marco Loog, Tom Viering, Alexander Mey

in NeurIPS 2019

code slides poster

A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization

A Mey, T Viering, M Loog

arxiv preprint 2019

Nuclear discrepancy for single-shot batch active learning

Tom J Viering, Jesse H Krijthe, Marco Loog

in Machine Learning 2019

code slides poster

Open Problem: Monotonicity of Learning

T Viering, A Mey, M Loog

in COLT 2019

Nuclear Discrepancy for Active Learning

Tom J Viering, Jesse H Krijthe, Marco Loog

arxiv preprint 2017

Generalization Bound Minimization for Active Learning

Tom J Viering, Jesse H Krijthe, Marco Loog

in Benelearn 2017

Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds

T J Viering

masterthesis 2016

In my lab, we often give short talks about selected papers we like. You can find the papers I've given talks about below.

During my masters in Delft I discovered my passion for Machine Learning. I spent a long time on my masters project, simply because I loved it so much. My supervisors were Marco Loog and Jesse Krijthe, and we studied the problem of Active Learning using generalization bounds, in particular using the Discrepancy measure. View the abstract of my master thesis.

While I really enjoyed physics in my bachelor, in the end of my bachelor I fell in love with computer science (CS). During my bachelor project I built an application to control an electron microscope (EM) to record a giant mosaic of images as fast and accurate as possible. I also worked together with Frank Faas to develop a basic application to annotate and view gigabyte-size EM images. You can view the zebrafish dataset of the KosterLab research group here, which was in part annotated with help of software that Frank and I wrote. I spent the fourth year of my (physics) bachelor studying Computer Science in order to switch to my CS master at TU Delft.