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 across epochs can require repeated sampling or recomputation. We introduce EDiS (Edge-Disjoint Subgraph sparsification framework), which se...
S. Navuluru, Siddhartha Shankar Das, Franck Dernoncourt et al.· 0 citations
GRS-QA is introduced, a reasoning graph-structured question answering dataset that augments multi-hop QA instances from exisiting datasets, with directed reasoning graphs representing intermediate inference steps, and highlights the importance of unified structural annotations for understanding and improving multi-hop...
Khin S. Yone, Devasha Trivedi, Anish Pahilajani et al.· International Conference on...· 0 citations
A common layer equation is introduced that represents covered architectures through seven components: an update domain, channel set, propagation bank, per-channel message maps, channel-fusion operator, ego/residual map, and update map, which exposes the empirical inverse problem of mapping measurable graph and task pro...
S. Navuluru, Siddhartha Shankar Das, B. Ni et al.· 0 citations
This work introduces the problem of personalized auto-research, which conditions every stage of the research process on a representation of the individual researcher, and proposes a general and flexible framework that threads a graph-grounded researcher context through retrieval, hypothesis search, experimentation, wri...
B. Ni, Franck Dernoncourt, Hong-Jie Chen et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
