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1 code implementation • 11 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.

1 code implementation • 22 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.

no code implementations • 18 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.

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.

no code implementations • 31 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.

no code implementations • 8 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.

no code implementations • 1 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.

no code implementations • 23 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.

no code implementations • 3 Jun 2016 • Michalis Michaelides, Dimitrios Milios, Jane Hillston, Guido Sanguinetti

Dynamical systems with large state-spaces are often expensive to thoroughly explore experimentally.

no code implementations • 7 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.

1 code implementation • 28 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

no code implementations • 8 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.

no code implementations • 18 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.

1 code implementation • 28 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.

no code implementations • 29 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.

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.

no code implementations • 3 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.

no code implementations • 24 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.

no code implementations • 17 May 2013 • Botond Cseke, Guido Sanguinetti

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

no code implementations • 17 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.

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.

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.

no code implementations • NeurIPS 2007 • Manfred Opper, Guido Sanguinetti

Markov jump processes play an important role in a large number of application domains.

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