Sivasubramanian’s core point is that agents aren’t a feature toggle but an architectural choice. The advantage goes to organizations that design for compounding momentum across work, security, ...
This repository supports the following paper: M. Zhang, P. Li, Y. Xia, K. Wang, and L. Jin, Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning. [PDF] SEAL ...
Abstract: Knowledge graphs are used extensively in various fields. Labor-intensive and time-consuming error detection for large-scale knowledge graphs significantly ...
Abstract: This paper proposes a graph-based matrix level partitioning methodology for parallel electromagnetic transient (EMT) simulation. By partitioning the graph associated with the admittance ...
Genomic medicine relies on single reference genomes that miss crucial genetic diversity, creating diagnostic gaps that disproportionately affect underrepresented populations. Pangenome graphs, ...
@article{article, author = {Chen, Yu and Shen, Shuhan and Chen, Yisong and Wang, Guoping}, year = {2020}, month = {07}, pages = {107537}, title = {Graph-Based ...
GRAPE is a software resource for graph processing, learning and embedding that is orders of magnitude faster than existing state-of-the-art libraries. GRAPE can quickly process real-world graphs with ...
Algorithms in new application areas like machine learning and network analysis use “irregular” data structures such as graphs, trees and sets. Writing efficient parallel code in these problem domains ...
In today's widely used parallel programming models, subtle programming errors can lead to unintended nondeterministic behavior and hard to catch bugs. In contrast, we argue for a parallel programming ...
Solving computationally hard problems using conventional computing architectures is often slow and energetically inefficient. Quantum computing may help with these challenges, but it is still in the ...
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