10 citations · 34 across the 10 of their papers we have counts for
10 papers
Automated Discovery of Laser Dicing Processes with Bayesian Optimization for Semiconductor Manufacturing
David Leeftink, Roman Doll, Heleen Visserman +4
Laser dicing of semiconductor wafers is a critical step in microelectronic manufacturing, where multiple sequential laser passes precisely separate individual dies from the wafer.…
Optimal Control of Probabilistic Dynamics Models via Mean Hamiltonian Minimization
David Leeftink, Çağatay Yıldız, Steffen Ridderbusch +2
Without exact knowledge of the true system dynamics, optimal control of non-linear continuous-time systems requires careful treatment under epistemic uncertainty. In this work, we…
Automatic structured variational inference
Luca Ambrogioni, Kate Lin, Emily Fertig +4
Stochastic variational inference offers an attractive option as a default method for differentiable probabilistic programming. However, the performance of the variational approach…
The Indian Chefs Process
Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier +6
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes India…
Bayesian nonparametric discontinuity design
Max Hinne, David Leeftink, Marcel A. J. van Gerven +1
Quasi-experimental research designs, such as regression discontinuity and interrupted time series, allow for causal inference in the absence of a randomized controlled trial, at th…
Wasserstein Variational Inference
Luca Ambrogioni, Umut Güçlü, Yağmur Güçlütürk +3
This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory. Wasserstein variational inference uses a ne…
Forward Amortized Inference for Likelihood-Free Variational Marginalization
Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya +5
In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss. The resulting forward amortize…
GP CaKe: Effective brain connectivity with causal kernels
Luca Ambrogioni, Max Hinne, Marcel van Gerven +1
A fundamental goal in network neuroscience is to understand how activity in one region drives activity elsewhere, a process referred to as effective connectivity. Here we propose t…
Efficient sampling of Gaussian graphical models using conditional Bayes factors
Max Hinne, Alex Lenkoski, Tom Heskes +1
Bayesian estimation of Gaussian graphical models has proven to be challenging because the conjugate prior distribution on the Gaussian precision matrix, the G-Wishart distribution,…
Bayesian Inference of Whole-Brain Networks
M. Hinne, T. Heskes, M. A. J. van Gerven
In structural brain networks the connections of interest consist of white-matter fibre bundles between spatially segregated brain regions. The presence, location and orientation of…