James T. Wilson | DeepAI

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∙ 09/02/2023

A Unifying Variational Framework for Gaussian Process Motion Planning

To control how a robot moves, motion planning algorithms must compute pa...

0 Lucas Cosier, et al. ∙

∙ 11/08/2020

Pathwise Conditioning of Gaussian Processes

As Gaussian processes are integrated into increasingly complex problem s...

0 James T. Wilson, et al. ∙

∙ 02/21/2020

Efficiently sampling functions from Gaussian process posteriors

Gaussian processes are the gold standard for many real-world modeling pr...

4 James T. Wilson, et al. ∙

∙ 05/25/2018

Maximizing acquisition functions for Bayesian optimization

Bayesian optimization is a sample-efficient approach to global optimizat...

0 James T. Wilson, et al. ∙

∙ 12/01/2017

The reparameterization trick for acquisition functions

Bayesian optimization is a sample-efficient approach to solving global o...

0 James T. Wilson, et al. ∙

∙ 06/14/2015

Compressing Convolutional Neural Networks

Convolutional neural networks (CNN) are increasingly used in many areas ...

0 Wenlin Chen, et al. ∙

∙ 04/19/2015

Compressing Neural Networks with the Hashing Trick

As deep nets are increasingly used in applications suited for mobile dev...

0 Wenlin Chen, et al. ∙

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