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Hans Roggeman

dblp:321/6631 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference
approximate bayesian inference		 0.612022
Numerical Approximations of Log Gaussian Cox Process (Student Abstract) · AAAI 2022
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes
gaussian process		 0.612022
Numerical Approximations of Log Gaussian Cox Process (Student Abstract) · AAAI 2022
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes › point process
log-gaussian cox process		 0.612022
Numerical Approximations of Log Gaussian Cox Process (Student Abstract) · AAAI 2022
Computational science and engineering › spatial statistics
spatiotemporal modeling		 0.212022
Numerical Approximations of Log Gaussian Cox Process (Student Abstract) · AAAI 2022

Methods — techniques the papers use, named apart from their topics

numerical approximation · 1.1nonparametric estimation · 0.6non-parametric estimation · 0.6
YearPublicationVenuePosition
2022 Numerical Approximations of Log Gaussian Cox Process (Student Abstract)
abstract
This paper considers a multi-state Log Gaussian Cox Process (`"LGCP'') on a graph, where transmissions amongst states are calibrated using a non-parametric approach. We thus consider multi-output LGCPs and introduce numerical approximations to compute posterior distributions extremely quickly and in a completely transparent and reproducible fashion. The model is tested on historical data and shows very good performance.
Francois Buet-Golfouse, Hans Roggeman
 AAAI2
2022 Robust Collaborative Learning for Sequence Modelling
abstract
Current deep learning techniques for RNA classification suffer from over-fitting and lack of reproducibility. We show that by introducing robustness by design in both CNN and RNN algorithms, we are able to achieve standalone state-of-the-art accuracy. By constructing model-agnostic robustness checks and reusing features obtained from both architectures, we build a collaborative framework that improves performance and stability.
Francois Buet-Golfouse, Hans Roggeman, Islam Utyagulov
 ICASSP2