Zobrazeno 1 - 10
of 541
pro vyhledávání: '"Sanguinetti, Guido"'
The spatial organization of chromatin within the nucleus plays a crucial role in gene expression and genome function. However, the quantitative relationship between this organization and nuclear biochemical processes remains under debate. In this stu
Externí odkaz:
http://arxiv.org/abs/2409.14425
Publikováno v:
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:88-98 (2023)
Pruning deep neural networks is a widely used strategy to alleviate the computational burden in machine learning. Overwhelming empirical evidence suggests that pruned models retain very high accuracy even with a tiny fraction of parameters. However,
Externí odkaz:
http://arxiv.org/abs/2306.12190
Publikováno v:
NeurIPS 2023
Machine learning models are famously vulnerable to adversarial attacks: small ad-hoc perturbations of the data that can catastrophically alter the model predictions. While a large literature has studied the case of test-time attacks on pre-trained mo
Externí odkaz:
http://arxiv.org/abs/2305.11132
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. Despite the availability of large data sets probing this process
Externí odkaz:
http://arxiv.org/abs/2210.11323
Autor:
Bortolussi, Luca, Carbone, Ginevra, Laurenti, Luca, Patane, Andrea, Sanguinetti, Guido, Wicker, Matthew
Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications. Despite significant efforts, both practical and theoretical, training deep learning models robust to adversarial at
Externí odkaz:
http://arxiv.org/abs/2207.06154
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. However the standard approach to modelling stoch
Externí odkaz:
http://arxiv.org/abs/2205.06268
We consider the problem of the stability of saliency-based explanations of Neural Network predictions under adversarial attacks in a classification task. Saliency interpretations of deterministic Neural Networks are remarkably brittle even when the a
Externí odkaz:
http://arxiv.org/abs/2102.11010
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. Both methods are independent of the
Externí odkaz:
http://arxiv.org/abs/2102.09230
Autor:
Sanguinetti, Guido
We consider the problem of estimating the reproduction number $R_t$ of an epidemic for populations where the probability of detection of cases depends on a known covariate. We argue that in such cases the normal empirical estimator can fail when the
Externí odkaz:
http://arxiv.org/abs/2012.02105
Publikováno v:
In Biophysical Journal 16 January 2024 123(2):184-194
