55 citations · 118 across the 12 of their papers we have counts for
6 papers · 1 filter
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
Geoff Pleiss, John P. Cunningham
Large width limits have been a recent focus of deep learning research: modulo computational practicalities, do wider networks outperform narrower ones? Answering this question has…
Rectangular Flows for Manifold Learning
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss +1
Normalizing flows are invertible neural networks with tractable change-of-volume terms, which allow optimization of their parameters to be efficiently performed via maximum likelih…
Simulating time to event prediction with spatiotemporal echocardiography deep learning
Rohan Shad, Nicolas Quach, Robyn Fong +8
Integrating methods for time-to-event prediction with diagnostic imaging modalities is of considerable interest, as accurate estimates of survival requires accounting for censoring…
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations
Luhuan Wu, Andrew Miller, Lauren Anderson +3
We examine the general problem of inter-domain Gaussian Processes (GPs): problems where the GP realization and the noisy observations of that realization lie on different domains.…
Predicting post-operative right ventricular failure using video-based deep learning
Rohan Shad, Nicolas Quach, Robyn Fong +17
Non-invasive and cost effective in nature, the echocardiogram allows for a comprehensive assessment of the cardiac musculature and valves. Despite progressive improvements over the…
Bias-Free Scalable Gaussian Processes via Randomized Truncations
Andres Potapczynski, Luhuan Wu, Dan Biderman +2
Scalable Gaussian Process methods are computationally attractive, yet introduce modeling biases that require rigorous study. This paper analyzes two common techniques: early trunca…