17 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- KU LeuvenBE9 papers
- VIB-KU Leuven Center for MicrobiologyBE6 papers
- Universidade Nova de LisboaPT2 papers
- Applied Biomathematics (United States)US1 paper
- Biomathematics and Statistics ScotlandGB1 paper
- Centro Hospitalar de Lisboa OcidentalPT1 paper
- Escola Superior de Saúde Egas MonizPT1 paper
- Fogarty International CenterUS1 paper
- Foxconn (Taiwan)TW1 paper
- Harvard UniversityUS1 paper
- Institut Montpelliérain Alexander GrothendieckFR1 paper
- Johns Hopkins UniversityUS1 paper
11 papers
Generative Kernel Spectral Clustering
David Winant, Sonny Achten, Johan A. K. Suykens
Modern clustering approaches often trade interpretability for performance, particularly in deep learning-based methods. We present Generative Kernel Spectral Clustering (GenKSC), a…
Finding Closure: A Closer Look at the Gestalt Law of Closure in Convolutional Neural Networks
Yuyan Zhang, Derya Soydaner, Lisa Koßmann +2
The human brain has an inherent ability to fill in gaps to perceive figures as complete wholes, even when parts are missing or fragmented. This phenomenon is known as Closure in ps…
A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1
Giulia Di Teodoro, Federico Siciliano, Valerio Guarrasi +6
Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This sc…
Quantum Speedups for Multiproposal MCMC
Chin-Yi Lin, Kuo-Chin Chen, Philippe Lemey +3
Multiproposal Markov chain Monte Carlo (MCMC) algorithms choose from multiple proposals to generate their next chain step in order to sample from challenging target distributions m…
Which constraints of a numerical problem cause ill-conditioning?
Nick Dewaele, Nick Vannieuwenhoven
Many numerical problems with input and output can be formulated as a system of equations where the goal is to solve for . The condition number measures the…
Group-invariant tensor train networks for supervised learning
Brent Sprangers, Nick Vannieuwenhoven
Invariance has recently proven to be a powerful inductive bias in machine learning models. One such class of predictive or generative models are tensor networks. We introduce a new…
Tensor completion using geodesics on Segre manifolds
Lars Swijsen, Joeri Van der Veken, Nick Vannieuwenhoven
We propose a Riemannian conjugate gradient (CG) optimization method for finding low rank approximations of incomplete tensors. Our main contribution consists of an explicit express…
The condition number of many tensor decompositions is invariant under Tucker compression
Nick Dewaele, Paul Breiding, Nick Vannieuwenhoven
We characterise the sensitivity of several additive tensor decompositions with respect to perturbations of the original tensor. These decompositions include canonical polyadic deco…
Large-scale inference of correlation among mixed-type biological traits with phylogenetic multivariate probit models
Zhenyu Zhang, Akihiko Nishimura, Paul Bastide +5
Inferring concerted changes among biological traits along an evolutionary history remains an important yet challenging problem. Besides adjusting for spurious correlation induced f…
Bayesian inference of set-point viral load transmission models
Pieter Libin, Laurens Hernalsteen, Kristof Theys +3
When modelling HIV epidemics, it is important to incorporate set-point viral load and its heritability. As set-point viral load distributions can differ significantly amongst epide…
Inferring Heterogeneous Evolutionary Processes Through Time: from sequence substitution to phylogeography
Filip Bielejec, Philippe Lemey, Guy Baele +2
Molecular phylogenetic and phylogeographic reconstructions generally assume time-homogeneous substitution processes. Motivated by computational convenience, this assumption sacrifi…