Search Results for author:
Found 5 papers, 2 papers with code
Learning in Deep Factor Graphs with Gaussian Belief Propagation
We propose an approach to do learning in Gaussian factor graphs.
Data augmentation in Bayesian neural networks and the cold posterior effect
Here, we provide several approaches to developing principled Bayesian neural networks incorporating data augmentation.
Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19
The statistical inference framework is implemented and compared on Intel Xeon CPU, NVIDIA Tesla V100 GPU and the Graphcore Mk1 IPU, and the results are discussed in the context of their computational architectures.
Parallel Training of Deep Networks with Local Updates
Deep learning models trained on large data sets have been widely successful in both vision and language domains.
Spatiotemporal Prediction of Ambulance Demand using Gaussian Process Regression
Accurately predicting when and where ambulance call-outs occur can reduce response times and ensure the patient receives urgent care sooner.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
