research
∙
05/30/2023
Adaptive Conditional Quantile Neural Processes
Neural processes are a family of probabilistic models that inherit the f...
share
research
∙
03/15/2022
MoReL: Multi-omics Relational Learning
Multi-omics data analysis has the potential to discover hidden molecular...
research
∙
12/15/2021
Bayesian Graph Contrastive Learning
Contrastive learning has become a key component of self-supervised learn...
research
∙
10/12/2020
BayReL: Bayesian Relational Learning for Multi-omics Data Integration
High-throughput molecular profiling technologies have produced high-dime...
research
∙
06/07/2020
Bayesian Graph Neural Networks with Adaptive Connection Sampling
We propose a unified framework for adaptive connection sampling in graph...
research
∙
04/16/2020
Network-principled deep generative models for designing drug combinations as graph sets
Combination therapy has shown to improve therapeutic efficacy while redu...
research
∙
10/28/2019
Semi-Implicit Stochastic Recurrent Neural Networks
Stochastic recurrent neural networks with latent random variables of com...
research
∙
08/26/2019
Variational Graph Recurrent Neural Networks
Representation learning over graph structured data has been mostly studi...
research
∙
08/19/2019
Semi-Implicit Graph Variational Auto-Encoders
Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to ex...
research
∙
07/03/2019
Spatially-Coupled Neural Network Architectures
In this work, we leverage advances in sparse coding techniques to reduce...
research
∙
01/10/2018
