Search Results for author: Simone Rossi

Found 8 papers, 0 papers with code

How Much is Enough? A Study on Diffusion Times in Score-based Generative Models

no code implementations10 Jun 2022 Giulio Franzese, Simone Rossi, Lixuan Yang, Alessandro Finamore, Dario Rossi, Maurizio Filippone, Pietro Michiardi

Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data.

Model Selection for Bayesian Autoencoders

no code implementations 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

Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations

no code implementations6 Mar 2020 Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone

Variational inference techniques based on inducing variables provide an elegant framework for scalable posterior estimation in Gaussian process (GP) models.

Gaussian Processes Variational Inference

Efficient Approximate Inference with Walsh-Hadamard Variational Inference

no code implementations29 Nov 2019 Simone Rossi, Sebastien Marmin, Maurizio Filippone

Variational inference offers scalable and flexible tools to tackle intractable Bayesian inference of modern statistical models like Bayesian neural networks and Gaussian processes.

Bayesian Inference Gaussian Processes +1

Walsh-Hadamard Variational Inference for Bayesian Deep Learning

no code implementations NeurIPS 2020 Simone Rossi, Sebastien Marmin, Maurizio Filippone

Over-parameterized models, such as DeepNets and ConvNets, form a class of models that are routinely adopted in a wide variety of applications, and for which Bayesian inference is desirable but extremely challenging.

Bayesian Inference Variational Inference

Good Initializations of Variational Bayes for Deep Models

no code implementations18 Oct 2018 Simone Rossi, Pietro Michiardi, Maurizio Filippone

Stochastic variational inference is an established way to carry out approximate Bayesian inference for deep models.

Bayesian Inference General Classification +1

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