The problem of data sparsity has long been a challenge in recommendation systems, and
previous studies have attempted to address this issue by incorporating side information …

The recent" pre-train, prompt, predict training" paradigm has gained popularity as a way to
learn generalizable models with limited labeled data. The approach involves using a pre …

Large language models (LLMs) have shown remarkable generalization capability with
exceptional performance in various language modeling tasks. However, they still exhibit …

Abstract Knowledge distillation involves transferring soft labels from a teacher to a student
using a shared temperature-based softmax function. However the assumption of a shared …

Graph neural networks (GNNs) are models that capture the dependencies between graph
data by passing messages between graph nodes and they have been widely used to …

Graph Neural Networks (GNNs) have demonstrated a great representation learning
capability on graph data and have been utilized in various downstream applications …

Abstract Graph Neural Networks (GNNs) conduct message passing which aggregates local
neighbors to update node representations. Such message passing leads to scalability …

Although graph neural networks have exhibited remarkable performance in various graph
tasks, a significant concern is their vulnerability to adversarial attacks. Consequently, many …

ABSTRACT Graph Neural Networks (GNNs) have achieved state-of-the-art results on a
variety of graph learning tasks, however, it has been demonstrated that they are vulnerable …

Relational learning has gained significant attention, led by the expressiveness of Graph
Neural Networks (GNNs) on graph data. While the inherent biases in common graph data …