Search Results for author: Guido Sanguinetti

Found 27 papers, 8 papers with code

Quantifying lottery tickets under label noise: accuracy, calibration, and complexity

1 code implementation21 Jun 2023 Viplove Arora, Daniele Irto, Sebastian Goldt, Guido Sanguinetti

Pruning deep neural networks is a widely used strategy to alleviate the computational burden in machine learning.

Attacks on Online Learners: a Teacher-Student Analysis

1 code implementation NeurIPS 2023 Riccardo Giuseppe Margiotta, Sebastian Goldt, Guido Sanguinetti

Machine learning models are famously vulnerable to adversarial attacks: small ad-hoc perturbations of the data that can catastrophically alter the model predictions.

Bottom-up data integration in polymer models of chromatin organisation

no code implementations20 Oct 2022 Alex Chen Yi Zhang, Angelo Rosa, Guido Sanguinetti

Cellular functions crucially depend on the precise execution of complex biochemical reactions taking place on the chromatin fiber in the tightly packed environment of the cell nucleus.

Bayesian Inference Data Integration

On the Robustness of Bayesian Neural Networks to Adversarial Attacks

1 code implementation13 Jul 2022 Luca Bortolussi, Ginevra Carbone, Luca Laurenti, Andrea Patane, Guido Sanguinetti, Matthew Wicker

Despite significant efforts, both practical and theoretical, training deep learning models robust to adversarial attacks is still an open problem.

Variational Inference

Bayesian learning of effective chemical master equations in crowded intracellular conditions

1 code implementation11 May 2022 Svitlana Braichenko, Ramon Grima, Guido Sanguinetti

Biochemical reactions inside living cells often occur in the presence of crowders -- molecules that do not participate in the reactions but influence the reaction rates through excluded volume effects.

Bayesian Optimisation

Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks

1 code implementation22 Feb 2021 Ginevra Carbone, Guido Sanguinetti, Luca Bortolussi

We empirically show that interpretations provided by Bayesian Neural Networks are considerably more stable under adversarial perturbations of the inputs and even under direct attacks to the explanations.

General Classification

Random Projections for Improved Adversarial Robustness

no code implementations18 Feb 2021 Ginevra Carbone, Guido Sanguinetti, Luca Bortolussi

We propose two training techniques for improving the robustness of Neural Networks to adversarial attacks, i. e. manipulations of the inputs that are maliciously crafted to fool networks into incorrect predictions.

Adversarial Robustness Dimensionality Reduction

Robustness of Bayesian Neural Networks to Gradient-Based Attacks

1 code implementation NeurIPS 2020 Ginevra Carbone, Matthew Wicker, Luca Laurenti, Andrea Patane, Luca Bortolussi, Guido Sanguinetti

Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications.

Variational Inference

Geometric fluid approximation for general continuous-time Markov chains

no code implementations31 Jan 2019 Michalis Michaelides, Jane Hillston, Guido Sanguinetti

We construct here a general method based on spectral analysis of the transition matrix of the CTMC, without the need for a population structure.

Intrinsic Geometric Vulnerability of High-Dimensional Artificial Intelligence

no code implementations8 Nov 2018 Luca Bortolussi, Guido Sanguinetti

The success of modern Artificial Intelligence (AI) technologies depends critically on the ability to learn non-linear functional dependencies from large, high dimensional data sets.

Vocal Bursts Intensity Prediction

Efficient Low-Order Approximation of First-Passage Time Distributions

no code implementations1 Jun 2017 David Schnoerr, Botond Cseke, Ramon Grima, Guido Sanguinetti

We consider the problem of computing first-passage time distributions for reaction processes modelled by master equations.

Bayesian Inference Sequential Bayesian Inference

Approximation and inference methods for stochastic biochemical kinetics - a tutorial review

no code implementations23 Aug 2016 David Schnoerr, Guido Sanguinetti, Ramon Grima

In summary, this review gives a self-contained introduction to modelling, approximations and inference methods for stochastic chemical kinetics.

Matching models across abstraction levels with Gaussian Processes

no code implementations7 May 2016 Giulio Caravagna, Luca Bortolussi, Guido Sanguinetti

Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study.

Gaussian Processes

Higher order methylation features for clustering and prediction in epigenomic studies

1 code implementation28 Mar 2016 Chantriolnt-Andreas Kapourani, Guido Sanguinetti

Using these higher order features across promoter-proximal regions, we are able to construct a powerful machine learning predictor of gene expression, significantly improving upon the predictive power of average DNA methylation levels.

Genomics Quantitative Methods

Cox process representation and inference for stochastic reaction-diffusion processes

no code implementations8 Jan 2016 David Schnoerr, Ramon Grima, Guido Sanguinetti

Our work provides both insights into spatio-temporal stochastic systems, and a practical solution to a long-standing problem in computational modelling.

Epidemiology Model Selection

Expectation propagation for continuous time stochastic processes

no code implementations18 Dec 2015 Botond Cseke, David Schnoerr, Manfred Opper, Guido Sanguinetti

We consider the inverse problem of reconstructing the posterior measure over the trajec- tories of a diffusion process from discrete time observations and continuous time constraints.

Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations

1 code implementation28 Sep 2015 Anastasis Georgoulas, Jane Hillston, Guido Sanguinetti

We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules.

Bayesian Inference

Learning Temporal Logical Properties Discriminating ECG models of Cardiac Arrhytmias

no code implementations29 Dec 2013 Ezio Bartocci, Luca Bortolussi, Guido Sanguinetti

We present a novel approach to learn the formulae characterising the emergent behaviour of a dynamical system from system observations.

Approximate inference in latent Gaussian-Markov models from continuous time observations

no code implementations NeurIPS 2013 Botond Cseke, Manfred Opper, Guido Sanguinetti

We propose an approximate inference algorithm for continuous time Gaussian-Markov process models with both discrete and continuous time likelihoods.

On the Robustness of Temporal Properties for Stochastic Models

no code implementations3 Sep 2013 Ezio Bartocci, Luca Bortolussi, Laura Nenzi, Guido Sanguinetti

By discussing two examples, we show how to approximate the distribution of the robustness score and its key indicators: the average robustness and the conditional average robustness.

A stochastic hybrid model of a biological filter

no code implementations24 Aug 2013 Andrea Ocone, Guido Sanguinetti

We present a hybrid model of a biological filter, a genetic circuit which removes fast fluctuations in the cell's internal representation of the extra cellular environment.

Factored expectation propagation for input-output FHMM models in systems biology

no code implementations17 May 2013 Botond Cseke, Guido Sanguinetti

We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications.

Variational Inference

Sparse Approximate Inference for Spatio-Temporal Point Process Models

no code implementations17 May 2013 Botond Cseke, Andrew Zammit Mangion, Tom Heskes, Guido Sanguinetti

Spatio-temporal point process models play a central role in the analysis of spatially distributed systems in several disciplines.

Inference in continuous-time change-point models

no code implementations NeurIPS 2011 Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor

We consider the problem of Bayesian inference for continuous time multi-stable stochastic systems which can change both their diffusion and drift parameters at discrete times.

Bayesian Inference Test +1

Approximate inference in continuous time Gaussian-Jump processes

no code implementations NeurIPS 2010 Manfred Opper, Andreas Ruttor, Guido Sanguinetti

We present a novel approach to inference in conditionally Gaussian continuous time stochastic processes, where the latent process is a Markovian jump process.

Gaussian Processes

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