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Found 26 papers, 9 papers with code
Conditioning Score-Based Generative Models by Neuro-Symbolic Constraints
Score-based and diffusion models have emerged as effective approaches for both conditional and unconditional generation.
CARSO: Blending Adversarial Training and Purification Improves Adversarial Robustness
In this work, we propose a novel adversarial defence mechanism for image classification - CARSO - blending the paradigms of adversarial training and adversarial purification in a mutually-beneficial, robustness-enhancing way.
Relating Implicit Bias and Adversarial Attacks through Intrinsic Dimension
Despite their impressive performance in classification, neural networks are known to be vulnerable to adversarial attacks.
Towards Invertible Semantic-Preserving Embeddings of Logical Formulae
State of the art Machine Learning (ML) approaches are mostly based on gradient descent optimisation in continuous spaces, while learning logic is framed in the discrete syntactic space of formulae.
Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes
We consider the problem of predictive monitoring (PM), i. e., predicting at runtime the satisfaction of a desired property from the current system's state.
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.
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking
As observations are costly and noisy, smMC is framed as a Bayesian inference problem so that the estimates have an additional quantification of the uncertainty.
Graph Neural Networks for Propositional Model Counting
Graph Neural Networks (GNNs) have been recently leveraged to solve several logical reasoning tasks.
Learning Model Checking and the Kernel Trick for Signal Temporal Logic on Stochastic Processes
We introduce a similarity function on formulae of signal temporal logic (STL).
Neural Predictive Monitoring under Partial Observability
We consider the problem of predictive monitoring (PM), i. e., predicting at runtime future violations of a system from the current state.
Abstraction of Markov Population Dynamics via Generative Adversarial Nets
Markov Population Models are a widespread formalism used to model the dynamics of complex systems, with applications in Systems Biology and many other fields.
Abstraction-Guided Truncations for Stationary Distributions of Markov Population Models
To understand the long-run behavior of Markov population models, the computation of the stationary distribution is often a crucial part.
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.
Analysis of Markov Jump Processes under Terminal Constraints
Many probabilistic inference problems such as stochastic filtering or the computation of rare event probabilities require model analysis under initial and terminal constraints.
A kernel function for Signal Temporal Logic formulae
We discuss how to define a kernel for Signal Temporal Logic (STL) formulae.
Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems
We introduce a novel learning-based approach to synthesize safe and robust controllers for autonomous Cyber-Physical Systems and, at the same time, to generate challenging tests.
Density-embedding layers: a general framework for adaptive receptive fields
In this paper, we propose the novel theoretical framework of density-embedded layers, generalizing the transformation represented by a neuron.
Improved estimations of stochastic chemical kinetics by finite state expansion
The deterministic rate equation (DRE) gives a macroscopic approximation as a compact system of differential equations that estimate the average populations for each species, but it may be inaccurate in the case of nonlinear interaction dynamics.
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.
Global Adversarial Robustness Guarantees for Neural Networks
We investigate global adversarial robustness guarantees for machine learning models.
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.
A Robust Genetic Algorithm for Learning Temporal Specifications from Data
We consider the problem of mining signal temporal logical requirements from a dataset of regular (good) and anomalous (bad) trajectories of a dynamical system.
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.
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.
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.
