149 citations · 539 across the 71 of their papers we have counts for
7 papers · 1 filter
On Linear Separability under Linear Compression with Applications to Hard Support Vector Machine
Paul McVay, Tie Liu, Krishna Narayanan
This paper investigates the theoretical problem of maintaining linear separability of the data-generating distribution under linear compression. While it has been long known that l…
BayReL: Bayesian Relational Learning for Multi-omics Data Integration
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield +2
High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale. Studying…
Bayesian Graph Neural Networks with Adaptive Connection Sampling
Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki +4
We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs. The p…
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…