Classifier evasion consists in finding for a given instance x the “nearest” instance x'such that
the classifier predictions of x and x'are different. We present two novel algorithms for …

Machine Learning (ML) algorithms, specifically supervised learning, are widely used in
modern real-world applications, which utilize Computational Intelligence (CI) as their core …

Multivariate pattern analysis techniques have been increasingly used over the past decade
to derive highly sensitive and specific biomarkers of diseases on an individual basis. The …

Miscreants register thousands of new domains every day to launch Internet-scale attacks,
such as spam, phishing, and drive-by downloads. Quickly and accurately determining a …

In this paper, we present a new embedded wavelet image coding system using quadtree
splitting and context modeling. It features low computational complexity and high …

In recent years, self-attention has become the dominant paradigm for sequence modeling in
a variety of domains. However, in domains with very long sequence lengths the $\mathcal …

There is a recent focus on designing architectures that have an Integer Linear Programming
(ILP) layer within a neural model (referred to as\emph {Neural ILP} in this paper). Neural ILP …

We introduce the problem of learning SMT (LRA) constraints from data. SMT (LRA) extends
propositional logic with (in) equalities between numerical variables. Many relevant formal …

This paper investigates a constrained formulation of neural networks where the output is a
convex function of the input. We show that the convexity constraints can be enforced on both …

We propose a family of quasi-linear discriminants that outperform current large-margin
methods in sliding window visual object detection and open set recognition tasks. In these …