1 code implementation • NeurIPS 2023 • Paul Rosa, Viacheslav Borovitskiy, Alexander Terenin, Judith Rousseau
Gaussian processes are used in many machine learning applications that rely on uncertainty quantification.
no code implementations • 1 Dec 2022 • Deborah Sulem, Vincent Rivoirard, Judith Rousseau
Hawkes processes are often applied to model dependence and interaction phenomena in multivariate event data sets, such as neuronal spike trains, social interactions, and financial transactions.
1 code implementation • 18 Mar 2022 • Cian Naik, Judith Rousseau, Trevor Campbell
Bayesian coresets approximate a posterior distribution by building a small weighted subset of the data points.
no code implementations • NeurIPS Workshop ICBINB 2021 • Soufiane Hayou, Arnaud Doucet, Judith Rousseau
Recent work by Jacot et al. (2018) has shown that training a neural network of any kind with gradient descent is strongly related to kernel gradient descent in function space with respect to the Neural Tangent Kernel (NTK).
no code implementations • 24 Oct 2020 • Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau
Deep ResNet architectures have achieved state of the art performance on many tasks.
no code implementations • 31 May 2019 • Soufiane Hayou, Arnaud Doucet, Judith Rousseau
Recent work by Jacot et al. (2018) has shown that training a neural network of any kind with gradient descent in parameter space is strongly related to kernel gradient descent in function space with respect to the Neural Tangent Kernel (NTK).
no code implementations • 19 Feb 2019 • Soufiane Hayou, Arnaud Doucet, Judith Rousseau
The weight initialization and the activation function of deep neural networks have a crucial impact on the performance of the training procedure.
no code implementations • ICLR 2019 • Soufiane Hayou, Arnaud Doucet, Judith Rousseau
We complete this analysis by providing quantitative results showing that, for a class of ReLU-like activation functions, the information propagates indeed deeper for an initialization at the edge of chaos.
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 • 5 Jun 2014 • Pierre Alquier, Vincent Cottet, Nicolas Chopin, Judith Rousseau
While the behaviour of algorithms based on nuclear norm minimization is now well understood, an as yet unexplored avenue of research is the behaviour of Bayesian algorithms in this context.