Deep neural networks for computer vision are deployed in increasingly safety-critical and
socially-impactful applications, motivating the need to close the gap in model performance …

In the past years, deep neural networks (DNN) have become popular in many disciplines
such as computer vision (CV), natural language processing (NLP), etc. The evolution of …

Deep neural networks (DNNs) are well-known to be vulnerable to adversarial attacks, where
malicious human-imperceptible perturbations are included in the input to the deep network …

Differentiable architecture search (DARTS) is a prevailing NAS solution to identify
architectures. Based on the continuous relaxation of the architecture space, DARTS learns a …

Abstract Model heterogeneous federated learning is a realistic and challenging problem.
However, due to the limitations of data collection, storage, and transmission conditions, as …

Neural diffusion on graphs is a novel class of graph neural networks that has attracted
increasing attention recently. The capability of graph neural partial differential equations …

The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning
varies depending on context and community. Researchers either focus on narrow technical …

We provide a general framework for studying recurrent neural networks (RNNs) trained by
injecting noise into hidden states. Specifically, we consider RNNs that can be viewed as …

This review article gives a high-level overview of the approaches across different scales of
organization and levels of abstraction. The studies covered in this paper include …

Deep Learning is currently used to perform multiple tasks, such as object recognition, face
recognition, and natural language processing. However, Deep Neural Networks (DNNs) are …