1 code implementation • 2 Feb 2023 • Francois Caron, Fadhel Ayed, Paul Jung, Hoil Lee, Juho Lee, Hongseok Yang
We consider the optimisation of large and shallow neural networks via gradient flow, where the output of each hidden node is scaled by some positive parameter.
no code implementations • NeurIPS 2017 • Andrei-Cristian Barbos, Francois Caron, Jean-François Giovannelli, Arnaud Doucet
We propose a generalized Gibbs sampler algorithm for obtaining samples approximately distributed from a high-dimensional Gaussian distribution.
no code implementations • 6 Jul 2016 • Cian Naik, Francois Caron, Judith Rousseau, Yee Whye Teh, Konstantina Palla
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks.
no code implementations • 9 Feb 2016 • Richard Yi Da Xu, Francois Caron, Arnaud Doucet
We introduce here a class of Bayesian nonparametric models to address this problem.
no code implementations • NeurIPS 2012 • Francois Caron
We develop a novel Bayesian nonparametric model for random bipartite graphs.
no code implementations • NeurIPS 2012 • Francois Caron, Yee W. Teh
We develop a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items.
no code implementations • NeurIPS 2009 • Francois Caron, Arnaud Doucet
In latent feature models, we associate to each data point a potentially infinite number of binary latent variables indicating the possession of some features and the IBP is a prior distribution on the associated infinite binary matrix.