62 citations · 117 across the 12 of their papers we have counts for
4 papers · 1 filter
Semi-Implicit Stochastic Recurrent Neural Networks
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield +3
Stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic d…
Variational Graph Recurrent Neural Networks
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield +3
Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant. In this paper, we dev…
Semi-Implicit Graph Variational Auto-Encoders
Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield +3
Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand the flexibility of variational graph auto-encoders (VGAE) to model graph data. SIG-VAE employs a hierar…
Spatially-Coupled Neural Network Architectures
Arman Hasanzadeh, Nagaraj T. Janakiraman, Vamsi K. Amalladinne +1
In this work, we leverage advances in sparse coding techniques to reduce the number of trainable parameters in a fully connected neural network. While most of the works in literatu…