Solving Trojan Detection Competitions with Linear Weight Classification

Neural networks can conceal malicious Trojan backdoors that allow a trigger to covertly change the model behavior. Detecting signs of these backdoors, particularly without access to any triggered data, is the subject of ongoing research and open challenges. In one common formulation of the problem, we are given a set of clean and poisoned models and need to predict whether a given test model is clean or poisoned. In this paper, we introduce a detector that works remarkably well across many of the existing datasets and domains. It is obtained by training a binary classifier on a large number of models' weights after performing a few different pre-processing steps including feature selection and standardization, reference model weights subtraction, and model alignment prior to detection. We evaluate this algorithm on a diverse set of Trojan detection benchmarks and domains and examine the cases where the approach is most and least effective.

Efficient Construction of Local Parametric Reduced Order Models Using Machine Learning Techniques

Reduced order models are computationally inexpensive approximations that capture the important dynamical characteristics of large, high-fidelity computer models of physical systems. This paper applies machine learning techniques to improve the design of parametric reduced order models. Specifically, machine learning is used to develop feasible regions in the parameter space where the admissible target accuracy is achieved with a predefined reduced order basis, to construct parametric maps, to chose the best two already existing bases for a new parameter configuration from accuracy point of view and to pre-select the optimal dimension of the reduced basis such as to meet the desired accuracy. By combining available information using bases concatenation and interpolation as well as high-fidelity solutions interpolation we are able to build accurate reduced order models associated with new parameter settings. Promising numerical results with a viscous Burgers model illustrate the potential of machine learning approaches to help design better reduced order models.