Guido Sanguinetti | DeepAI

AI Chat

Featured Co-authors

Ezio Bartocci
43 publications
Tom Heskes
38 publications
Luca Bortolussi
33 publications
Manfred Opper
31 publications
Andrew Zammit Mangion
25 publications
Luca Laurenti
24 publications
Matthew Wicker
19 publications
Sebastian Goldt
19 publications
Andrea Patane
14 publications
Laura Nenzi
12 publications
Botond Cseke
10 publications

∙ 06/21/2023

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

Pruning deep neural networks is a widely used strategy to alleviate the ...

0 Viplove Arora, et al. ∙

∙ 05/18/2023

Attacks on Online Learners: a Teacher-Student Analysis

Machine learning models are famously vulnerable to adversarial attacks: ...

0 Riccardo Giuseppe Margiotta, et al. ∙

∙ 07/13/2022

On the Robustness of Bayesian Neural Networks to Adversarial Attacks

Vulnerability to adversarial attacks is one of the principal hurdles to ...

0 Luca Bortolussi, et al. ∙

∙ 02/22/2021

Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks

We consider the problem of the stability of saliency-based explanations ...

0 Ginevra Carbone, et al. ∙

∙ 02/18/2021

Random Projections for Improved Adversarial Robustness

We propose two training techniques for improving the robustness of Neura...

0 Ginevra Carbone, et al. ∙

∙ 12/01/2020

Systematic errors in estimates of R_t from symptomatic cases in the presence of observation bias

We consider the problem of estimating the reproduction number R_t of an ...

0 Guido Sanguinetti, et al. ∙

∙ 02/11/2020

Robustness of Bayesian Neural Networks to Gradient-Based Attacks

Vulnerability to adversarial attacks is one of the principal hurdles to ...

7 Ginevra Carbone, et al. ∙

∙ 01/31/2019

Geometric fluid approximation for general continuous-time Markov chains

Fluid approximations have seen great success in approximating the macro-...

6 Michalis Michaelides, et al. ∙

∙ 11/08/2018

Intrinsic Geometric Vulnerability of High-Dimensional Artificial Intelligence

The success of modern Artificial Intelligence (AI) technologies depends ...

0 Luca Bortolussi, et al. ∙

∙ 11/06/2017

Probabilistic Model Checking for Continuous Time Markov Chains via Sequential Bayesian Inference

Probabilistic model checking for systems with large or unbounded state s...

0 Dimitrios Milios, et al. ∙

∙ 06/07/2017

On learning the structure of Bayesian Networks and submodular function maximization

Learning the structure of dependencies among multiple random variables i...

0 Giulio Caravagna, et al. ∙

∙ 06/01/2017

Efficient Low-Order Approximation of First-Passage Time Distributions

We consider the problem of computing first-passage time distributions fo...

0 David Schnoerr, et al. ∙

∙ 08/23/2016

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

Stochastic fluctuations of molecule numbers are ubiquitous in biological...

0 David Schnoerr, et al. ∙

∙ 06/03/2016

Property-driven State-Space Coarsening for Continuous Time Markov Chains

Dynamical systems with large state-spaces are often expensive to thoroug...

0 Michalis Michaelides, et al. ∙

∙ 05/07/2016

Matching models across abstraction levels with Gaussian Processes

Biological systems are often modelled at different levels of abstraction...

0 Giulio Caravagna, et al. ∙

∙ 01/08/2016

Cox process representation and inference for stochastic reaction-diffusion processes

Complex behaviour in many systems arises from the stochastic interaction...

0 David Schnoerr, et al. ∙

∙ 12/18/2015

Expectation propagation for continuous time stochastic processes

We consider the inverse problem of reconstructing the posterior measure ...

0 Botond Cseke, et al. ∙

∙ 09/28/2015

Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations

We consider continuous time Markovian processes where populations of ind...

0 Anastasis Georgoulas, et al. ∙

∙ 12/29/2013

Learning Temporal Logical Properties Discriminating ECG models of Cardiac Arrhytmias

We present a novel approach to learn the formulae characterising the eme...

0 Ezio Bartocci, et al. ∙

∙ 09/03/2013

On the Robustness of Temporal Properties for Stochastic Models

Stochastic models such as Continuous-Time Markov Chains (CTMC) and Stoch...

0 Ezio Bartocci, et al. ∙

∙ 05/17/2013

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

We consider the problem of joint modelling of metabolic signals and gene...

0 Botond Cseke, et al. ∙

∙ 05/17/2013

Sparse Approximate Inference for Spatio-Temporal Point Process Models

Spatio-temporal point process models play a central role in the analysis...

0 Botond Cseke, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world