98 citations · 130 across the 6 of their papers we have counts for
6 papers
Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh
Tian Qin, Alex Beatson, Deniz Oktay +2
Partial differential equations (PDEs) are often computationally challenging to solve, and in many settings many related PDEs must be be solved either at every timestep or for a var…
Randomized Automatic Differentiation
Deniz Oktay, Nick McGreivy, Joshua Aduol +2
The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to comput…
Learning Composable Energy Surrogates for PDE Order Reduction
Alex Beatson, Jordan T. Ash, Geoffrey Roeder +2
Meta-materials are an important emerging class of engineered materials in which complex macroscopic behaviour--whether electromagnetic, thermal, or mechanical--arises from modular…
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Yucen Luo, Alex Beatson, Mohammad Norouzi +4
Standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest. We introduce an unbiased estimator of the log margin…
Efficient Optimization of Loops and Limits with Randomized Telescoping Sums
Alex Beatson, Ryan P. Adams
We consider optimization problems in which the objective requires an inner loop with many steps or is the limit of a sequence of increasingly costly approximations. Meta-learning,…
Continual Learning in Generative Adversarial Nets
Ari Seff, Alex Beatson, Daniel Suo +1
Developments in deep generative models have allowed for tractable learning of high-dimensional data distributions. While the employed learning procedures typically assume that trai…