Implicit Variational Inference: the Parameter and the Predictor Space

no code implementations • 24 Oct 2020 • Yann Pequignot, Mathieu Alain, Patrick Dallaire, Alireza Yeganehparast, Pascal Germain, Josée Desharnais, François Laviolette

Relying on a family of distributions given by a deep generative neural network, we present two ways of carrying variational inference: one in \emph{parameter space}, one in \emph{predictor space}.

Variational Inference

The Indian Chefs Process

no code implementations • 29 Jan 2020 • Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Brahim Chaib-Draa, Marcel van Gerven, Francois Laviolette

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 Indian Buffet Processes.

Tree bark re-identification using a deep-learning feature descriptor

1 code implementation • 6 Dec 2019 • Martin Robert, Patrick Dallaire, Philippe Giguère

Oftentimes, it relies on collections of visual signatures based on descriptors, such as SIFT or SURF.