Search Results for author: A. Stephen McGough

Found 15 papers, 1 papers with code

Towards Automatic Cetacean Photo-Identification: A Framework for Fine-Grain, Few-Shot Learning in Marine Ecology

no code implementations7 Dec 2022 Cameron Trotter, Nick Wright, A. Stephen McGough, Matt Sharpe, Barbara Cheney, Mònica Arso Civil, Reny Tyson Moore, Jason Allen, Per Berggren

Photo-identification (photo-id) is one of the main non-invasive capture-recapture methods utilised by marine researchers for monitoring cetacean (dolphin, whale, and porpoise) populations.

Few-Shot Learning

The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets

no code implementations7 Mar 2021 Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw

Through the prediction of electricity demand profile over the next 24h, we can simulate the predictions made for a day-ahead market.

Exploring market power using deep reinforcement learning for intelligent bidding strategies

no code implementations8 Nov 2020 Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough

If any single generator company, or a collaborating group of generator companies, control more than ${\sim}$11$\%$ of generation capacity and bid strategically, prices begin to increase by ${\sim}$25$\%$.

reinforcement-learning reinforcement Learning

NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation

no code implementations27 May 2020 Cameron Trotter, Georgia Atkinson, Matt Sharpe, Kirsten Richardson, A. Stephen McGough, Nick Wright, Ben Burville, Per Berggren

We introduce the Northumberland Dolphin Dataset 2020 (NDD20), a challenging image dataset annotated for both coarse and fine-grained instance segmentation and categorisation.

Instance Segmentation Semantic Segmentation

Optimising energy and overhead for large parameter space simulations

no code implementations6 Oct 2019 Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough

A Pareto frontier can be used to identify the sets of optimal parameters for which each is the `best' for a given combination of objectives -- thus allowing decisions to be made with full knowledge.

The Northumberland Dolphin Dataset: A Multimedia Individual Cetacean Dataset for Fine-Grained Categorisation

no code implementations7 Aug 2019 Cameron Trotter, Georgia Atkinson, Matthew Sharpe, A. Stephen McGough, Nick Wright, Per Berggren

Methods for cetacean research include photo-identification (photo-id) and passive acoustic monitoring (PAM) which generate thousands of images per expedition that are currently hand categorised by researchers into the individual dolphins sighted.

An Exploration of Dropout with RNNs for Natural Language Inference

no code implementations22 Oct 2018 Amit Gajbhiye, Sardar Jaf, Noura Al Moubayed, A. Stephen McGough, Steven Bradley

In this paper, we propose a novel RNN model for NLI and empirically evaluate the effect of applying dropout at different layers in the model.

Natural Language Inference

Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments

no code implementations19 Oct 2018 A. Stephen McGough, Matthew Forshaw, John Brennan, Noura Al Moubayed, Stephen Bonner

We demonstrate, through the use of simulation, how we can reduce this wasted energy by targeting tasks at computers less likely to be needed for primary use, predicting this idle time through machine learning.

BIG-bench Machine Learning

Black-box Variational Inference for Stochastic Differential Equations

2 code implementations ICML 2018 Thomas Ryder, Andrew Golightly, A. Stephen McGough, Dennis Prangle

Parameter inference for stochastic differential equations is challenging due to the presence of a latent diffusion process.

Variational Inference

SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder

no code implementations17 Jun 2016 Noura Al Moubayed, Toby Breckon, Peter Matthews, A. Stephen McGough

In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages.

Denoising Spam detection

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