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Found 9 papers, 5 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.
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.
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.
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.
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.
ETC-NLG: End-to-end Topic-Conditioned Natural Language Generation
We first test the effectiveness of our approach in a low-resource setting for Italian, evaluating the conditioning for both topic models and gold annotations.
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.
