The last decade saw an enormous boost in the field of computational topology: methods and
concepts from algebraic and differential topology, formerly confined to the realm of pure …

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to
understand and design deep learning models. This paper posits that TDL may complement …

Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks,
yet have been shown to be oblivious to eminent substructures such as cycles. We present …

Graph Neural Networks (GNNs) are proven to be a powerful machinery for learning complex
dependencies in multivariate spatio-temporal processes. However, most existing GNNs …

Graph generative model evaluation necessitates understanding differences between graphs
on the distributional level. This entails being able to harness salient attributes of graphs in …

Link prediction is an important learning task for graph-structured data. In this paper, we
propose a novel topological approach to characterize interactions between two nodes. Our …

Deep neural networks are known to have security issues. One particular threat is the Trojan
attack. It occurs when the attackers stealthily manipulate the model's behavior through …

Graph neural networks (GNNs) offer a new powerful alternative for multivariate time series
forecasting, demonstrating remarkable success in a variety of spatio-temporal applications …

The problem of (point) forecasting univariate time series is considered. Most approaches,
ranging from traditional statistical methods to recent learning-based techniques with neural …

In computer-aided drug discovery (CADD), virtual screening (VS) is used for comparing a
library of compounds against known active ligands to identify the drug candidates that are …