292 citations
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8 papers · 1 filter
Focal points and their implications for Möbius Transforms and Dempster-Shafer Theory
Maxime Chaveroche, Franck Davoine, Véronique Cherfaoui
Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a much higher computational burden. A lot of work has…
Learning from missing data with the Latent Block Model
Gabriel Frisch, Jean-Benoist Léger, Yves Grandvalet
Missing data can be informative. Ignoring this information can lead to misleading conclusions when the data model does not allow information to be extracted from the missing data.…
Decentralized Multi-Agent Pursuit using Deep Reinforcement Learning
Cristino de Souza, Rhys Newbury, Akansel Cosgun +3
Pursuit-evasion is the problem of capturing mobile targets with one or more pursuers. We use deep reinforcement learning for pursuing an omni-directional target with multiple, homo…
EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering
Lianmeng Jiao, Thierry Denoeux, Zhun-ga Liu +1
The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference. In this paper, we propose a new mode…
NN-EVCLUS: Neural Network-based Evidential Clustering
Thierry Denoeux
Evidential clustering is an approach to clustering based on the use of Dempster-Shafer mass functions to represent cluster-membership uncertainty. In this paper, we introduce a neu…
Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning
Abdelhak Loukkal, Yves Grandvalet, Tom Drummond +1
Camera-based end-to-end driving neural networks bring the promise of a low-cost system that maps camera images to driving control commands. These networks are appealing because the…