99 citations · 188 across the 22 of their papers we have counts for
4 papers · 1 filter
FastSample: Accelerating Distributed Graph Neural Network Training for Billion-Scale Graphs
Hesham Mostafa, Adam Grabowski, Md Asadullah Turja +3
Training Graph Neural Networks(GNNs) on a large monolithic graph presents unique challenges as the graph cannot fit within a single machine and it cannot be decomposed into smaller…
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE
Marius Arvinte, Cory Cornelius, Jason Martin +1
Beyond their impressive sampling capabilities, score-based diffusion models offer a powerful analysis tool in the form of unbiased density estimation of a query sample under the tr…
Resource-Efficient Federated Hyperdimensional Computing
Nikita Zeulin, Olga Galinina, Nageen Himayat +1
In conventional federated hyperdimensional computing (HDC), training larger models usually results in higher predictive performance but also requires more computational, communicat…
Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks
Aleksei Ponomarenko-Timofeev, Olga Galinina, Ravikumar Balakrishnan +3
Federated systems enable collaborative training on highly heterogeneous data through model personalization, which can be facilitated by employing multi-task learning algorithms. Ho…