3 papers
cs.LG2023
DYAD: A Descriptive Yet Abjuring Density efficient approximation to linear neural network layers
Sarin Chandy, Varun Gangal, Yi Yang +1
We devise, implement and performance-asses DYAD, a layer which can serve as a faster and more memory-efficient approximate replacement for linear layers, (nn.Linear() in Pytorch).…
cs.LG2019
Enhanced Variational Inference with Dyadic Transformation
Sarin Chandy, Amin Rasekh
Variational autoencoder is a powerful deep generative model with variational inference. The practice of modeling latent variables in the VAE's original formulation as normal distri…
cs.CR2018
Cyberattack Detection using Deep Generative Models with Variational Inference
Sarin E. Chandy, Amin Rasekh, Zachary A. Barker +1
Recent years have witnessed a rise in the frequency and intensity of cyberattacks targeted at critical infrastructure systems. This study designs a versatile, data-driven cyberatta…