1 code implementation • 27 Nov 2017 • Wiktor Olszowy, John Aston, Catarina Rua, Guy B. Williams
The reliability of task fMRI studies would increase with more accurate autocorrelation modeling.
Quantitative Methods
1 code implementation • 16 Jun 2017 • Bin Dai, Yu Wang, John Aston, Gang Hua, David Wipf
Variational autoencoders (VAE) represent a popular, flexible form of deep generative model that can be stochastically fit to samples from a given random process using an information-theoretic variational bound on the true underlying distribution.
3 code implementations • 19 Jun 2014 • Fredrik Lindsten, Adam M. Johansen, Christian A. Naesseth, Bonnie Kirkpatrick, Thomas B. Schön, John Aston, Alexandre Bouchard-Côté
We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models.
no code implementations • 4 Sep 2012 • Roman V. Belavkin, Alastair Channon, Elizabeth Aston, John Aston, Rok Krasovec, Christopher G. Knight
This theoretical result motivates our hypothesis that optimal mutation rate functions in such landscapes will increase when fitness decreases in some neighbourhood of an optimum, resembling the control functions derived in the geometric case.