Aston University

Neural networks for pattern recognition

From the Publisher: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the bas...

Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines

ABSTRACT The last decade has seen a sharp increase in the number of scientific publications describing physiological and pathological functions of extracellular vesicles (EVs), ...

Using the framework method for the analysis of qualitative data in multi-disciplinary health research

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Enhanced power solitons in optical fibres withperiodic dispersion management

The authors examine the formation of stable soliton-like pulses in optical fibres with a periodic dispersion map. It is found that increased energy is required to launch a pulse...

Stable soliton-like propagation in dispersion managedsystems with net anomalous, zero and normaldispersion

The authors have discovered from numerical modelling that there are stable nonlinear transmission pulses for periodically dispersion managed systems where the path average dispe...

Computation with Infinite Neural Networks

For neural networks with a wide class of weight priors, it can be shown that in the limit of an infinite number of hidden units, the prior over functions tends to a gaussian pro...

Paediatric ‘care closer to home’: Stake-holder views and barriers to implementation

Gaussian regression and optimal finite dimensional linear models

The problem of regression under Gaussian assumptions is treated generally. The relationship between Bayesian prediction, regularization and smoothing is elucidated. The ideal re...

Finite-Dimensional Approximation of Gaussian Processes

Gaussian process (GP) prediction suffers from O(n3) scaling with the data set size n. By using a finite-dimensional basis to approximate the GP predictor, the computational comp...