no code implementations • 24 Jan 2023 • Michail Gkagkos, Krishna R. Narayanan, Jean-Francois Chamberland, Costas N. Georghiades
The goal is to create a low complexity, linear compression strategy, called PolarAir, that reduces the size of the gradient at the user side to lower the number of channel uses needed to transmit it.
2 code implementations • NeurIPS 2019 • Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R. Narayanan, Mingyuan Zhou, Xiaoning Qian
Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant.
Ranked #2 on Dynamic Link Prediction on DBLP Temporal
1 code implementation • NeurIPS 2019 • Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield, Krishna R. Narayanan, Mingyuan Zhou, Xiaoning Qian
Compared to VGAE, the derived graph latent representations by SIG-VAE are more interpretable, due to more expressive generative model and more faithful inference enabled by the flexible semi-implicit construction.
no code implementations • 3 Jul 2019 • Arman Hasanzadeh, Nagaraj T. Janakiraman, Vamsi K. Amalladinne, Krishna R. Narayanan
In this work, we leverage advances in sparse coding techniques to reduce the number of trainable parameters in a fully connected neural network.
no code implementations • 19 Nov 2017 • Arman Hasanzadeh, Xi Liu, Nick Duffield, Krishna R. Narayanan, Byron Chigoy
Building a prediction model for transportation networks is challenging because spatio-temporal dependencies of traffic data in different roads are complex and the graph constructed from road networks is very large.