Search Results for author: Amos J. Storkey

Found 17 papers, 5 papers with code

Learning to Learn By Self-Critique

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.

Few-Shot Learning

GINN: Geometric Illustration of Neural Networks

1 code implementation2 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.

CINIC-10 is not ImageNet or CIFAR-10

1 code implementation2 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.

Image Classification

Continuously tempered Hamiltonian Monte Carlo

2 code implementations11 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

Asymptotically exact inference in differentiable generative models

1 code implementation25 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.

Stochastic Parallel Block Coordinate Descent for Large-scale Saddle Point Problems

no code implementations23 Nov 2015 Zhanxing Zhu, Amos J. Storkey

We consider convex-concave saddle point problems with a separable structure and non-strongly convex functions.

Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems

no code implementations12 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.

The supervised hierarchical Dirichlet process

no code implementations17 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.

Renewal Strings for Cleaning Astronomical Databases

no code implementations7 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.

Continuous Relaxations for Discrete Hamiltonian Monte Carlo

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.

The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes

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.

Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability

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.

Sparse Instrumental Variables (SPIV) for Genome-Wide Studies

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.

Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model

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.

Continuous Time Particle Filtering for fMRI

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).

Modelling motion primitives and their timing in biologically executed movements

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.

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