30 citations · 106 across the 11 of their papers we have counts for
12 papers
The Shape of Learning Curves: a Review
Tom Viering, Marco Loog
Learning curves provide insight into the dependence of a learner's generalization performance on the training set size. This important tool can be used for model selection, to pred…
ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility
Burak Yildiz, Hayley Hung, Jesse H. Krijthe +9
We present ReproducedPapers.org: an open online repository for teaching and structuring machine learning reproducibility. We evaluate doing a reproduction project among students an…
Respecting Domain Relations: Hypothesis Invariance for Domain Generalization
Ziqi Wang, Marco Loog, Jan van Gemert
In domain generalization, multiple labeled non-independent and non-identically distributed source domains are available during training while neither the data nor the labels of tar…
Black Magic in Deep Learning: How Human Skill Impacts Network Training
Kanav Anand, Ziqi Wang, Marco Loog +1
How does a user's prior experience with deep learning impact accuracy? We present an initial study based on 31 participants with different levels of experience. Their task is to pe…
A Brief Prehistory of Double Descent
Marco Loog, Tom Viering, Alexander Mey +2
In their thought-provoking paper [1], Belkin et al. illustrate and discuss the shape of risk curves in the context of modern high-complexity learners. Given a fixed training sample…
Making Learners (More) Monotone
Tom J. Viering, Alexander Mey, Marco Loog
Learning performance can show non-monotonic behavior. That is, more data does not necessarily lead to better models, even on average. We propose three algorithms that take a superv…