Deep Learning

Generalisable Agents for Neural Network Optimisation
Optimising deep neural networks is a challenging task due to complex training dynamics, high computational requirements, and long …
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Revisiting the Role of Relearning in Semantic Dementia
Patients with semantic dementia (SD) present with remarkably consistent atrophy of neurons in the anterior temporal lobe and …
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On The Specialization of Neural Modules
A number of machine learning models have been proposed with the goal of achieving systematic generalization: the ability to reason …
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Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization
Training sparse networks to converge to the same performance as dense neural architectures has proven to be elusive. Recent work …
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If Dropout Limits Trainable Depth, Does Critical Initialisation Still Matter? A Large-scale Statistical Analysis on ReLU Networks
Recent work in signal propagation theory has shown that dropout limits the depth to which information can propagate through a neural …
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Inter-and Intra-domain Knowledge Transfer for Related Tasks in Deep Character Recognition
Pre-training a deep neural network on the ImageNet dataset is a common practice for training deep learning models, and generally yields …
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Quantisation and Pruning for Neural Network Compression and Regularisation
Deep neural networks are typically too computationally expensive to run in real-time on consumer-grade hardware and low-powered …
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