Search Results for author: Simone Rossi

Found 11 papers, 2 papers with code

Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning

no code implementations23 Nov 2023 Marc Schachtsiek, Simone Rossi, Thomas Hannagan

The more balanced labels increase minority class performance, which in turn allows the model to outperform the previous baseline by 0. 6, 1. 7, and 2. 4 mIoU for budgets of 5%, 10%, and 20%, respectively.

Active Learning Semantic Segmentation

Continuous-Time Functional Diffusion Processes

1 code implementation NeurIPS 2023 Giulio Franzese, Giulio Corallo, Simone Rossi, Markus Heinonen, Maurizio Filippone, Pietro Michiardi

We introduce Functional Diffusion Processes (FDPs), which generalize score-based diffusion models to infinite-dimensional function spaces.

Image Generation

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.

Computational Efficiency

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

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 +2

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