Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …

Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

With the growing use of graph convolutional neural networks (GCNNs) comes the need for
explainability. In this paper, we introduce explainability methods for GCNNs. We develop the …

Code generation models based on the pre-training and fine-tuning paradigm have been
increasingly attempted by both academia and industry, resulting in well-known industrial …

In this tutorial, we provide a didactic treatment of the emerging topic of signal processing on
higher-order networks. Drawing analogies from discrete and graph signal processing, we …

In this work, we make the first attempt to evaluate LLMs in a more challenging code
generation scenario, ie class-level code generation. We first manually construct the first …

Two architectures that generalize convolutional neural networks (CNNs) for the processing
of signals supported on graphs are introduced. We start with the selection graph neural …

We study the problem of selecting the best sampling set for bandlimited reconstruction of
signals on graphs. A frequency domain representation for graph signals can be defined …

Modern neuroimaging techniques provide us with unique views on brain structure and
function; ie, how the brain is wired, and where and when activity takes place. Data acquired …