The neuroscience of perception has recently been revolutionized with an integrative
modeling approach in which computation, brain function, and behavior are linked across …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Although machine learning is widely used in practice, little is known about practitioners'
understanding of potential security challenges. In this work, we close this substantial gap …

To survive, animals must adapt synaptic weights based on external stimuli and rewards. And
they must do so using local, biologically plausible, learning rules--a highly nontrivial …

The neuroscience of perception has recently been revolutionized with an integrative
modeling approach in which computation, brain function, and behavior are linked across …

In this article, we approach to construct reliable deep neural networks (DNNs) for safety-
critical artificial intelligent applications. We propose to modify rectified linear unit (ReLU), a …

Deep Neural Network has proved its potential in various perception tasks and hence
become an appealing option for interpretation and data processing in security sensitive …

Deep neural networks (DNNs) have achieved superior performance in various prediction
tasks, but can be very vulnerable to adversarial examples or perturbations. Therefore, it is …

Abstract Although deep Reinforcement Learning (RL) has proven successful in a wide range
of tasks, one challenge it faces is interpretability when applied to real-world problems …

Convolutional neural networks (CNNs) is a category of deep neural networks that are
primarily used for classifying image data. Yet, their continuous gain in popularity poses …