5 papers
EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks
Sai Karthik Navuluru, Siddhartha Shankar Das, Franck Dernoncourt +7
Sparse GNN training reduces computation, but deciding which edges to keep can be costly. Reusing one sparse graph is cheap, but locks training to a fixed topology, while varying it…
Joint and Cross-Modal Video-Audio Generation and Editing: A Unified Formulation and Design Taxonomy
Abhinav Sharma, Sai Karthik Navuluru, Wang Wei +21
Video and audio are perceived together, yet most generative models treat them in isolation. We examine methods that model the two modalities jointly, generate one from the other, o…
Scaffold: Support Graph Theory Based Sparsification for Graph Neural Networks
Siddhartha Shankar Das, Sai Karthik Navuluru, S M Ferdous +7
Graph neural networks (GNNs) rely on message passing over graph edges, making their computational and memory costs strongly dependent on graph density. Graph sparsification offers…
Unifying Graph Neural Networks Through a Common Layer Equation
Sai Karthik Navuluru, Siddhartha Shankar Das, Bo Ni +9
Graph neural networks are commonly described through family-specific equations whose notation obscures shared computations and structural differences. We introduce a common layer e…
AEGIS: A Multi-Task Joint-Embedding Predictive Architecture for Mammography
Scott Chase Waggener, Sai Karthik Navuluru, Lakshman Tamil
We present Aegis, a joint-embedding predictive architecture for breast cancer detection and density assessment in mammography. We train three Vision Transformer variants (Small/Bas…