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In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the(More)
MOTIVATION With the development of experimental methods and technology, we are able to reliably gain access to data in larger quantities, dimensions and types. This has great potential for the improvement of machine learning (as the learning algorithms have access to a larger space of information). However, conventional machine learning approaches used thus(More)
Framework The Expectation Maximisation (EM) algorithm is a procedure that iteratively optimises parameters of a given model, to maximise the likelihood of observing a given (training) dataset. 2. (E step) Compute the expression for the expected value of the likelihood, where the unobserved data's probability distribution is derived conditional on the(More)
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