Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

The interdependence between nodes in graphs is key to improve class prediction on nodes,
utilized in approaches like Label Probagation (LP) or in Graph Neural Networks (GNNs) …

In this overview paper, data-driven learning model-based cooperative localization and
location data processing are considered, in line with the emerging machine learning and big …

Semi-supervised learning (SSL) has been widely explored in recent years, and it is an
effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this …

Gaussian processes are a versatile framework for learning unknown functions in a manner
that permits one to utilize prior information about their properties. Although many different …

When faced with distribution shift at test time, deep neural networks often make inaccurate
predictions with unreliable uncertainty estimates. While improving the robustness of neural …

Graph classification is a critical task in numerous multimedia applications, where graphs are
employed to represent diverse types of multimedia data, including images, videos, and …

Abstract Current Graph Neural Networks (GNN) architectures generally rely on two important
components: node features embedding through message passing, and aggregation with a …

Most existing AU detection works considering AU relationships are relying on probabilistic
graphical models with manually extracted features. This paper proposes an end-to-end …

Recently, part-based deep models have achieved promising performance in person re-
identification (Re-ID), yet these models ignore the inter-local relationship of the …