6 citations · 7 across the 3 of their papers we have counts for
5 papers
Deep equilibrium models as estimators for continuous latent variables
Russell Tsuchida, Cheng Soon Ong
Principal Component Analysis (PCA) and its exponential family extensions have three components: observations, latents and parameters of a linear transformation. We consider a gener…
Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning
Changkun Ye, Nick Barnes, Lars Petersson +1
Zero-Shot Learning (ZSL) models aim to classify object classes that are not seen during the training process. However, the problem of class imbalance is rarely discussed, despite i…
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks
Russell Tsuchida, Tim Pearce, Chris van der Heide +2
Analysing and computing with Gaussian processes arising from infinitely wide neural networks has recently seen a resurgence in popularity. Despite this, many explicit covariance fu…
Richer priors for infinitely wide multi-layer perceptrons
Russell Tsuchida, Fred Roosta, Marcus Gallagher
It is well-known that the distribution over functions induced through a zero-mean iid prior distribution over the parameters of a multi-layer perceptron (MLP) converges to a Gaussi…
Exchangeability and Kernel Invariance in Trained MLPs
Russell Tsuchida, Fred Roosta, Marcus Gallagher
In the analysis of machine learning models, it is often convenient to assume that the parameters are IID. This assumption is not satisfied when the parameters are updated through t…