no code implementations • NeurIPS 2007 • Ben Williams, Marc Toussaint, Amos J. Storkey
Inference of the shape and the timing of primitives can be done using a factorial HMM based model, allowing the handwriting to be represented in primitive timing space.
no code implementations • NeurIPS 2007 • Lawrence Murray, Amos J. Storkey
We construct a biologically motivated stochastic differential model of the neural and hemodynamic activity underlying the observed Blood Oxygen Level Dependent (BOLD) signal in Functional Magnetic Resonance Imaging (fMRI).
no code implementations • NeurIPS 2010 • Paul Mckeigue, Jon Krohn, Amos J. Storkey, Felix V. Agakov
This paper describes a probabilistic framework for studying associations between multiple genotypes, biomarkers, and phenotypic traits in the presence of noise and unobserved confounders for large genetic studies.
no code implementations • NeurIPS 2010 • Peggy Series, David P. Reichert, Amos J. Storkey
The Charles Bonnet Syndrome (CBS) is characterized by complex vivid visual hallucinations in people with, primarily, eye diseases and no other neurological pathology.
no code implementations • NeurIPS 2011 • David P. Reichert, Peggy Series, Amos J. Storkey
Based on recent developments in machine learning, we show how neuronal adaptation can be understood as a mechanism that improves probabilistic, sampling-based inference.
no code implementations • NeurIPS 2012 • Simon Lyons, Amos J. Storkey, Simo Särkkä
The decomposition allows us to take a diffusion process of interest and cast it in a form that is amenable to sampling by MCMC methods.
no code implementations • NeurIPS 2012 • Yichuan Zhang, Zoubin Ghahramani, Amos J. Storkey, Charles A. Sutton
Continuous relaxations play an important role in discrete optimization, but have not seen much use in approximate probabilistic inference.
no code implementations • 7 Aug 2014 • Amos J. Storkey, Nigel C. Hambly, Christopher K. I. Williams, Robert G. Mann
Large astronomical databases obtained from sky surveys such as the SuperCOSMOS Sky Surveys (SSS) invariably suffer from a small number of spurious records coming from artefactual effects of the telescope, satellites and junk objects in orbit around earth and physical defects on the photographic plate or CCD.
no code implementations • 17 Dec 2014 • Andrew M. Dai, Amos J. Storkey
However, until now, Hierarchical Dirichlet Process (HDP) mixtures have not seen significant use in supervised problems with grouped data since a straightforward application of the HDP on the grouped data results in learnt clusters that are not predictive of the responses.
no code implementations • 12 Jun 2015 • Zhanxing Zhu, Amos J. Storkey
We consider a generic convex-concave saddle point problem with separable structure, a form that covers a wide-ranged machine learning applications.
no code implementations • NeurIPS 2015 • Xiaocheng Shang, Zhanxing Zhu, Benedict Leimkuhler, Amos J. Storkey
Monte Carlo sampling for Bayesian posterior inference is a common approach used in machine learning.
no code implementations • 23 Nov 2015 • Zhanxing Zhu, Amos J. Storkey
We consider convex-concave saddle point problems with a separable structure and non-strongly convex functions.
1 code implementation • 25 May 2016 • Matthew M. Graham, Amos J. Storkey
We use the intuition that inference corresponds to integrating a density across the manifold corresponding to the set of inputs consistent with the observed outputs.
2 code implementations • 11 Apr 2017 • Matthew M. Graham, Amos J. Storkey
Hamiltonian Monte Carlo (HMC) is a powerful Markov chain Monte Carlo (MCMC) method for performing approximate inference in complex probabilistic models of continuous variables.
Computation
2 code implementations • 2 Oct 2018 • Luke N. Darlow, Elliot J. Crowley, Antreas Antoniou, Amos J. Storkey
In this brief technical report we introduce the CINIC-10 dataset as a plug-in extended alternative for CIFAR-10.
Ranked #6 on Image Classification on CINIC-10
1 code implementation • 2 Oct 2018 • Luke N. Darlow, Amos J. Storkey
This informal technical report details the geometric illustration of decision boundaries for ReLU units in a three layer fully connected neural network.
1 code implementation • NeurIPS 2019 • Antreas Antoniou, Amos J. Storkey
In this paper, we propose a framework called \emph{Self-Critique and Adapt} or SCA.