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04/23/2022
SIReN-VAE: Leveraging Flows and Amortized Inference for Bayesian Networks
Initial work on variational autoencoders assumed independent latent vari...
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04/23/2022
Graphical Residual Flows
Graphical flows add further structure to normalizing flows by encoding n...
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09/01/2020
Performance-Agnostic Fusion of Probabilistic Classifier Outputs
We propose a method for combining probabilistic outputs of classifiers t...
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11/17/2019
Stochastic Gradient Annealed Importance Sampling for Efficient Online Marginal Likelihood Estimation
We consider estimating the marginal likelihood in settings with independ...
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10/23/2019
Stabilising priors for robust Bayesian deep learning
Bayesian neural networks (BNNs) have developed into useful tools for pro...
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10/13/2019
If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks
Recent work in signal propagation theory has shown that dropout limits t...
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10/12/2019
On the expected behaviour of noise regularised deep neural networks as Gaussian processes
Recent work has established the equivalence between deep neural networks...
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05/17/2019
A Coordinated Search Strategy for Multiple Solitary Robots: An Extension
The problem of coordination without a priori information about the envir...
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05/17/2019
A Coordinated Search Strategy for Solitary Robots
The problem of coordination without a priori information about the envir...
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11/01/2018
Critical initialisation for deep signal propagation in noisy rectifier neural networks
Stochastic regularisation is an important weapon in the arsenal of a dee...
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06/14/2018