Judith Rousseau

According to our database, Judith Rousseau authored at least 11 papers between 2009 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds.

Paul Rosa, Viacheslav Borovitskiy, Alexander Terenin, Judith Rousseau

CoRR, 2023

Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds.

Paul Rosa, Slava Borovitskiy, Alexander Terenin, Judith Rousseau

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Bayesian Nonparametrics for Sparse Dynamic Networks.

Cian Naik, François Caron, Judith Rousseau, Yee Whye Teh, Konstantina Palla

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement.

Cian Naik, Judith Rousseau, Trevor Campbell

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

The Curse of Depth in Kernel Regime.

Soufiane Hayou, Arnaud Doucet, Judith Rousseau

Proceedings of the I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 2021

Stable ResNet.

Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2019

Sparse Networks with Core-Periphery Structure.

Cian Naik, François Caron, Judith Rousseau

CoRR, 2019

Training Dynamics of Deep Networks using Stochastic Gradient Descent via Neural Tangent Kernel.

Soufiane Hayou, Arnaud Doucet, Judith Rousseau

CoRR, 2019

On the Impact of the Activation function on Deep Neural Networks Training.

Soufiane Hayou, Arnaud Doucet, Judith Rousseau

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

On the Selection of Initialization and Activation Function for Deep Neural Networks.

Soufiane Hayou, Arnaud Doucet, Judith Rousseau

CoRR, 2018

2009

Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis.

Darfiana Nur, David Allingham, Judith Rousseau, Kerrie L. Mengersen, Ross McVinish

Comput. Stat. Data Anal., 2009