3 citations · 7 across the 10 of their papers we have counts for
5 papers · 1 filter
VS-Graph: Scalable and Efficient Graph Classification Using Hyperdimensional Computing
Hamed Poursiami, Shay Snyder, Guojing Cong +2
Graph classification is a fundamental task in domains ranging from molecular property prediction to materials design. While graph neural networks (GNNs) achieve strong performance…
HyperGraphX: Graph Transductive Learning with Hyperdimensional Computing and Message Passing
Guojing Cong, Tom Potok, Hamed Poursiami +1
We present a novel algorithm, \hdgc, that marries graph convolution with binding and bundling operations in hyperdimensional computing for transductive graph learning. For predicti…
Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks
Ayana Moshruba, Hamed Poursiami, Maryam Parsa
Biological neurons exhibit diverse temporal spike patterns, which are believed to support efficient, robust, and adaptive neural information processing. While models such as Izhike…
On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levels
Ayana Moshruba, Shay Snyder, Hamed Poursiami +1
As machine learning models increasingly process sensitive data, understanding their vulnerability to privacy attacks is vital. Membership inference attacks (MIAs) exploit model res…
Do Spikes Protect Privacy? Investigating Black-Box Model Inversion Attacks in Spiking Neural Networks
Hamed Poursiami, Ayana Moshruba, Maryam Parsa
As machine learning models become integral to security-sensitive applications, concerns over data leakage from adversarial attacks continue to rise. Model Inversion (MI) attacks po…