As graph neural networks (GNNs) struggle with large-scale graphs due to high
computational demands, data distillation for graph data promises to alleviate this issue by …

Deep neural networks are becoming increasingly pervasive in academia and industry,
matching and surpassing human performance on a wide variety of fields and related tasks …

Running Graph Neural Networks (GNNs) on large graphs suffers from notoriously
inefficiency. This is attributed to the sparse graph-based operations, which is hard to be …

A deep geological repository for radioactive waste, such as Andra's Cigéo project, requires
long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are …

Deep neural networks are ubiquitously adopted in many applications, such as computer
vision, natural language processing, and graph analytics. However, well-trained neural …

Recommendation systems play a pivotal role in suggesting items to users based on their
preferences. However, in online platforms, these systems inevitably offer unsuitable …

Deep learning on graphs has garnered considerable attention across various machine
learning applications, encompassing social science, transportation services, and biomedical …

In recent years, Machine Learning (ML), especially deep neural networks, has achieved
remarkable success in fields like computer vision, natural language processing, graph …

The widespread use of machine learning in various applications has raised concerns about
ensuring fairness, particularly in sensitive scenarios where life-changing decisions are …