1k citations · 5.3k across the 132 of their papers we have counts for
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Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy +3
Gaussian processes are machine learning models capable of learning unknown functions in a way that represents uncertainty, thereby facilitating construction of optimal decision-mak…
Powerpropagation: A sparsity inducing weight reparameterisation
Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu +2
The training of sparse neural networks is becoming an increasingly important tool for reducing the computational footprint of models at training and evaluation, as well enabling th…
BayesIMP: Uncertainty Quantification for Causal Data Fusion
Siu Lun Chau, Jean-François Ton, Javier González +2
While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging. In this paper, we stud…
On Incorporating Inductive Biases into VAEs
Ning Miao, Emile Mathieu, N. Siddharth +2
We explain why directly changing the prior can be a surprisingly ineffective mechanism for incorporating inductive biases into VAEs, and introduce a simple and effective alternativ…
On Contrastive Representations of Stochastic Processes
Emile Mathieu, Adam Foster, Yee Whye Teh
Learning representations of stochastic processes is an emerging problem in machine learning with applications from meta-learning to physical object models to time series. Typical m…