5 accepted papers
Federated Graph Learning (FGL) has emerged as a powerful paradigm for decentralized training of graph neural networks while preserving data privacy. However, existing FGL methods are predominantly designed for static graphs and rely on parameter averaging or distribution alignment, which implicitly
Continual test-time adaptation (CTTA) seeks to adapt a source-pretrained model to continuously shifting target distributions. Recent advances in this field either minimize prediction entropy on streaming target samples, which is efficient but prone to error accumulation, or rely on teacher–student p
Task vectors capture how a model changes during fine-tuning by recording the difference between pre-trained and task-specific weights. The composition of task vectors, a key operator in task arithmetic, enables models to integrate knowledge from multiple tasks without incurring significant additiona…
Spoken language identification (SLI) by human beings relies on the hierarchical understanding of one or a few words within the voice signal, encapsulated within the corresponding time windows. Concurrently, frequency-domain features play a crucial role in enhancing identification. The short-time Fou…
Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in the graph, their ST patterns can vary greatly in difficulties for modeling, owning to the heterogeneous nature of ST data. We argu…