20 citations · 41 across the 10 of their papers we have counts for
10 papers
Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families
Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
We introduce squared neural Poisson point processes (SNEPPPs) by parameterising the intensity function by the squared norm of a two layer neural network. When the hidden layer is f…
Squared Neural Families: A New Class of Tractable Density Models
Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
Flexible models for probability distributions are an essential ingredient in many machine learning tasks. We develop and investigate a new class of probability distributions, which…
Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey
Abdelwahed Khamis, Russell Tsuchida, Mohamed Tarek +2
Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth century and has led to a plethora of methods for answering many theoretical and applied ques…
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…
Gaussian Process Bandits with Aggregated Feedback
Mengyan Zhang, Russell Tsuchida, Cheng Soon Ong
We consider the continuum-armed bandits problem, under a novel setting of recommending the best arms within a fixed budget under aggregated feedback. This is motivated by applicati…
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…
Invariance of Weight Distributions in Rectified MLPs
Russell Tsuchida, Farbod Roosta-Khorasani, Marcus Gallagher
An interesting approach to analyzing neural networks that has received renewed attention is to examine the equivalent kernel of the neural network. This is based on the fact that a…