arXiv subjects

Jack Baker

Publications and source records attributed to Jack Baker.

5 recordsLinked to original sources

arXiv2026

Neuron Surface Emitting Laser (NeuronSEL): Spiking Regimes and Negative Differential Resistance in Solitary Multi-junction VCSELs

Neuromorphic photonics is emerging as a powerful platform for fast and efficient optical information processing and sensing. However, future brain-inspired photonic systems require compact and scalable light sources, capable of generating the neuro-mimetic optical signals needed for their operation. This work demonstrates a single-stack laser that delivers optical and electrical neural-like spiking emission under solitary operation. Termed the Neuron Surface-Emitting Laser (NeuronSEL), this compact, multi-junction Vertical-Cavity Surface Emitting Laser (VCSEL) exhibits non-linear Negative Differential Resistance (NDR), similar to that observed in memristive devices. Leveraging this NDR behaviour enables the novel demonstration of multiple neuronal features in the NeuronSEL including refractoriness and threshold-/integrate-and-fire dynamics. We demonstrate the NeuronSEL's behaviour as an optical spiking neuron and its ability to perform processing functions, such as coincidence detection and exclusive OR operations. Its scalability is illustrated by proposing a network based on an array of NeuronSELs, able to perform classification tasks. The NeuronSEL emerges as a strong candidate for practical and scalable neuromorphic photonic hardware, with potential impact across a range of applications in optical sensing, communications and computing technologies, whilst benefitting from the inherent advantages of VCSEL technology -low manufacturing cost, compactness, efficiency, vertical emission, and straightforward integration into large arrayed-structures and networks.

physics.optics↗

arXiv2020

Large scale and linear scaling DFT with the CONQUEST code

We survey the underlying theory behind the large-scale and linear scaling DFT code, Conquest, which shows excellent parallel scaling and can be applied to thousands of atoms with exact solutions, and millions of atoms with linear scaling. We give details of the representation of the density matrix and the approach to finding the electronic ground state, and discuss the implementation of molecular dynamics with linear scaling. We give an overview of the performance of the code, focussing in particular on the parallel scaling, and provide examples of recent developments and applications.

physics.comp-ph↗

arXiv2018

Large-Scale Stochastic Sampling from the Probability Simplex

Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popular method for scalable Bayesian inference. These methods are based on sampling a discrete-time approximation to a continuous time process, such as the Langevin diffusion. When applied to distributions defined on a constrained space the time-discretization error can dominate when we are near the boundary of the space. We demonstrate that because of this, current SGMCMC methods for the simplex struggle with sparse simplex spaces; when many of the components are close to zero. Unfortunately, many popular large-scale Bayesian models, such as network or topic models, require inference on sparse simplex spaces. To avoid the biases caused by this discretization error, we propose the stochastic Cox-Ingersoll-Ross process (SCIR), which removes all discretization error and we prove that samples from the SCIR process are asymptotically unbiased. We discuss how this idea can be extended to target other constrained spaces. Use of the SCIR process within a SGMCMC algorithm is shown to give substantially better performance for a topic model and a Dirichlet process mixture model than existing SGMCMC approaches.

stat.CO↗

arXiv2017

sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo

This paper introduces the R package sgmcmc; which can be used for Bayesian inference on problems with large datasets using stochastic gradient Markov chain Monte Carlo (SGMCMC). Traditional Markov chain Monte Carlo (MCMC) methods, such as Metropolis-Hastings, are known to run prohibitively slowly as the dataset size increases. SGMCMC solves this issue by only using a subset of data at each iteration. SGMCMC requires calculating gradients of the log likelihood and log priors, which can be time consuming and error prone to perform by hand. The sgmcmc package calculates these gradients itself using automatic differentiation, making the implementation of these methods much easier. To do this, the package uses the software library TensorFlow, which has a variety of statistical distributions and mathematical operations as standard, meaning a wide class of models can be built using this framework. SGMCMC has become widely adopted in the machine learning literature, but less so in the statistics community. We believe this may be partly due to lack of software; this package aims to bridge this gap.

stat.CO↗

arXiv2017

Control Variates for Stochastic Gradient MCMC

It is well known that Markov chain Monte Carlo (MCMC) methods scale poorly with dataset size. A popular class of methods for solving this issue is stochastic gradient MCMC. These methods use a noisy estimate of the gradient of the log posterior, which reduces the per iteration computational cost of the algorithm. Despite this, there are a number of results suggesting that stochastic gradient Langevin dynamics (SGLD), probably the most popular of these methods, still has computational cost proportional to the dataset size. We suggest an alternative log posterior gradient estimate for stochastic gradient MCMC, which uses control variates to reduce the variance. We analyse SGLD using this gradient estimate, and show that, under log-concavity assumptions on the target distribution, the computational cost required for a given level of accuracy is independent of the dataset size. Next we show that a different control variate technique, known as zero variance control variates can be applied to SGMCMC algorithms for free. This post-processing step improves the inference of the algorithm by reducing the variance of the MCMC output. Zero variance control variates rely on the gradient of the log posterior; we explore how the variance reduction is affected by replacing this with the noisy gradient estimate calculated by SGMCMC.

stat.CO↗