Search Results for author: Ba-Hien Tran

Found 6 papers, 3 papers with code

Spatial Bayesian Neural Networks

1 code implementation16 Nov 2023 Andrew Zammit-Mangion, Michael D. Kaminski, Ba-Hien Tran, Maurizio Filippone, Noel Cressie

We also show that a single SBNN can be used to represent a variety of spatial processes often used in practice, such as Gaussian processes and lognormal processes.

Gaussian Processes

One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models

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.

Density Estimation

Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes

no code implementations9 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.

Gaussian Processes Representation Learning

Model Selection for Bayesian Autoencoders

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.

Model Selection Representation Learning

All You Need is a Good Functional Prior for Bayesian Deep Learning

no code implementations25 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.

Gaussian Processes

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