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07/06/2023
Beyond Intuition, a Framework for Applying GPs to Real-World Data
Gaussian Processes (GPs) offer an attractive method for regression over ...
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05/28/2023
Bayesian inference and neural estimation of acoustic wave propagation
In this work, we introduce a novel framework which combines physics and ...
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05/25/2023
Neural Characteristic Activation Value Analysis for Improved ReLU Network Feature Learning
We examine the characteristic activation values of individual ReLU units...
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11/22/2022
Learning Deep Neural Networks by Iterative Linearisation
The excellent real-world performance of deep neural networks has receive...
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10/14/2022
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
As Gaussian processes mature, they are increasingly being deployed as pa...
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09/28/2021
Interoperability of statistical models in pandemic preparedness: principles and reality
We present "interoperability" as a guiding framework for statistical mod...
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04/11/2021
Couplings for Multinomial Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) is a popular sampling method in Bayesian i...
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02/07/2020
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
We present the preliminary high-level design and features of DynamicPPL....
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06/22/2019
On the Secrecy Rate of Spatial Modulation Based Indoor Visible Light Communications
In this paper, we investigate the physical-layer security for a spatial ...
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05/26/2019
Bayesian Learning of Sum-Product Networks
Sum-product networks (SPNs) are flexible density estimators and have rec...
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07/26/2018
Adaptive Spatial Modulation for Visible Light Communications with an Arbitrary Number of Transmitters
As a power and bandwidth efficient modulation scheme, the optical spatia...
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09/16/2015
Dirichlet Fragmentation Processes
Tree structures are ubiquitous in data across many domains, and many dat...
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05/03/2015
