Search Results for author: Michael Smith

Found 4 papers, 0 papers with code

Uncertainty estimation in Deep Learning for Panoptic segmentation

no code implementations4 Apr 2023 Michael Smith, Frank Ferrie

This is the case with panoptic segmentation, where the structure of the problem and architectures designed to solve it means that unlike image classification or even semantic segmentation, the typical solution of using a mean across samples cannot be directly applied.

Image Classification Panoptic Segmentation +1

Adjoint-aided inference of Gaussian process driven differential equations

no code implementations9 Feb 2022 Paterne Gahungu, Christopher W Lanyon, Mauricio A Alvarez, Engineer Bainomugisha, Michael Smith, Richard D. Wilkinson

In this paper we show how the adjoint of a linear system can be used to efficiently infer forcing functions modelled as GPs, using a truncated basis expansion of the GP kernel.

Bayesian Inference Bayesian Optimisation

Amanuensis: The Programmer's Apprentice

no code implementations29 Jun 2018 Thomas Dean, Maurice Chiang, Marcus Gomez, Nate Gruver, Yousef Hindy, Michelle Lam, Peter Lu, Sophia Sanchez, Rohun Saxena, Michael Smith, Lucy Wang, Catherine Wong

This document provides an overview of the material covered in a course taught at Stanford in the spring quarter of 2018.

How Does Twitter User Behavior Vary Across Demographic Groups?

no code implementations WS 2017 Zach Wood-Doughty, Michael Smith, David Broniatowski, Mark Dredze

Demographically-tagged social media messages are a common source of data for computational social science.

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