arXiv AI
Jul 29

Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance

arXiv:2607. 25376v1 Announce Type: cross Abstract: In Bayesian neural networks (BNNs), variational inference is a widely adopted framework for modeling uncertainty in a distributional way, with the evidence lower bound (ELBO) serving as the standard objective function.

By Pei-Hsuan Hsia, Lars H. Heyen, Arvid Weyrauch, Markus Goetz, Achim Streit, Sebastian Krumscheid, Charlotte Debus
arXiv Machine Learning
Sep 22

SiST-GNN: Simultaneous Spatial-Temporal Message Passing for Dynamic Graph Representation Learning

SiST‑GNN introduces a simultaneous spatial‑temporal message‑passing framework for dynamic graph neural networks, fusing per‑node temporal embeddings with spatial aggregation in a single operation. By maintaining a recurrent hidden state per node and treating it as a cross‑time edge, the model jointly reasons over topology and evolution. Experiments on link‑prediction and node‑classification benchmarks show significant improvements over prior methods, achieving up to 158% gains in live‑update link prediction and outperforming discrete‑time baselines by 7–23% in dynamic node classification.

By Shubhajit Roy, Anirban Dasgupta