Search Results for author:
Found 27 papers, 8 papers with code
Quantifying lottery tickets under label noise: accuracy, calibration, and complexity
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
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
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.
On the Robustness of Bayesian Neural Networks to Adversarial Attacks
Despite significant efforts, both practical and theoretical, training deep learning models robust to adversarial attacks is still an open problem.
Bayesian learning of effective chemical master equations in crowded intracellular conditions
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.
Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks
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.
Random Projections for Improved Adversarial Robustness
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.
Robustness of Bayesian Neural Networks to Gradient-Based Attacks
Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications.
Geometric fluid approximation for general continuous-time Markov chains
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
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.
Efficient Low-Order Approximation of First-Passage Time Distributions
We consider the problem of computing first-passage time distributions for reaction processes modelled by master equations.
Approximation and inference methods for stochastic biochemical kinetics - a tutorial review
In summary, this review gives a self-contained introduction to modelling, approximations and inference methods for stochastic chemical kinetics.
Property-driven State-Space Coarsening for Continuous Time Markov Chains
Dynamical systems with large state-spaces are often expensive to thoroughly explore experimentally.
Matching models across abstraction levels with Gaussian Processes
Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study.
Higher order methylation features for clustering and prediction in epigenomic studies
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
Our work provides both insights into spatio-temporal stochastic systems, and a practical solution to a long-standing problem in computational modelling.
Expectation propagation for continuous time stochastic processes
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
We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules.
Learning Temporal Logical Properties Discriminating ECG models of Cardiac Arrhytmias
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
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
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
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
We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications.
Sparse Approximate Inference for Spatio-Temporal Point Process Models
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
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.
Approximate inference in continuous time Gaussian-Jump processes
We present a novel approach to inference in conditionally Gaussian continuous time stochastic processes, where the latent process is a Markovian jump process.
Variational inference for Markov jump processes
Markov jump processes play an important role in a large number of application domains.
