Abstract: Graph Neural Networks (GNNs) have aroused increasing research attention for their effectiveness on graph mining tasks. However, full-batch training methods based on stochastic gradient ...

This is the official implementation of the paper, titled "Neural Importance Sampling of Many Lights", to be presented at ACM SIGGRAPH 2025 (conference track). We propose a hybrid neural approach for ...

Abstract: In this paper, we analyze the effects of random sampling on adaptive diffusion networks. These networks consist in a collection of nodes that can measure and process data, and that can ...