1 code implementation • 16 Nov 2023 • Andrew Zammit-Mangion, Michael D. Kaminski, Ba-Hien Tran, Maurizio Filippone, Noel Cressie
We propose several variants of SBNNs, most of which are able to match the finite-dimensional distribution of the target process at the selected grid better than conventional BNNs of similar complexity.
1 code implementation • NeurIPS 2023 • Ba-Hien Tran, Giulio Franzese, Pietro Michiardi, Maurizio Filippone
Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impressive realistic-looking images.
no code implementations • 9 Feb 2023 • Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone
Autoencoders and their variants are among the most widely used models in representation learning and generative modeling.
1 code implementation • NeurIPS 2021 • Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone
We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization.
no code implementations • 25 Nov 2020 • Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Maurizio Filippone
This poses a challenge because modern neural networks are characterized by a large number of parameters, and the choice of these priors has an uncontrolled effect on the induced functional prior, which is the distribution of the functions obtained by sampling the parameters from their prior distribution.
no code implementations • pproximateinference AABI Symposium 2021 • Ba-Hien Tran, Dimitrios Milios, Simone Rossi, Maurizio Filippone
The Bayesian treatment of neural networks dictates that a prior distribution is considered over the weight and bias parameters of the network.