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12/07/2022
Towards Automatic Cetacean Photo-Identification: A Framework for Fine-Grain, Few-Shot Learning in Marine Ecology
Photo-identification (photo-id) is one of the main non-invasive capture-...
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10/31/2022
Explainable Deep Learning to Profile Mitochondrial Disease Using High Dimensional Protein Expression Data
Mitochondrial diseases are currently untreatable due to our limited unde...
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09/10/2021
Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning
A lowering in the cost of batteries and solar PV systems has led to a hi...
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03/07/2021
The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets
Electricity supply must be matched with demand at all times. This helps ...
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11/08/2020
Exploring market power using deep reinforcement learning for intelligent bidding strategies
Decentralized electricity markets are often dominated by a small set of ...
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05/28/2020
Optimizing carbon tax for decentralized electricity markets using an agent-based model
Averting the effects of anthropogenic climate change requires a transiti...
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05/27/2020
NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation
We introduce the Northumberland Dolphin Dataset 2020 (NDD20), a challeng...
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05/17/2020
Long-term electricity market agent based model validation using genetic algorithm based optimization
Electricity market modelling is often used by governments, industry and ...
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10/22/2019
ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning
Due to the threat of climate change, a transition from a fossil-fuel bas...
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10/06/2019
Optimising energy and overhead for large parameter space simulations
Many systems require optimisation over multiple objectives, where object...
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08/07/2019
The Northumberland Dolphin Dataset: A Multimedia Individual Cetacean Dataset for Fine-Grained Categorisation
Methods for cetacean research include photo-identification (photo-id) an...
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11/20/2018
Black-Box Autoregressive Density Estimation for State-Space Models
State-space models (SSMs) provide a flexible framework for modelling tim...
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10/22/2018
An Exploration of Dropout with RNNs for Natural Language Inference
Dropout is a crucial regularization technique for the Recurrent Neural N...
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10/19/2018
Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments
High Throughput Computing (HTC) provides a convenient mechanism for runn...
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02/09/2018
Black-box Variational Inference for Stochastic Differential Equations
Parameter inference for stochastic differential equations is challenging...
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06/17/2016