The popularity of neural networks that explicitly utilise the global correlation structure of their features have become vastly more popular ever since the Transformer architecture was developed. We propose to embed unsupervised Self-Organising Maps within neural networks as a means to model the global correlation structure. By enforcing topological preservation therein, such a neural network is able to represent more complex correlation structures and to produce interpretable visualisations as a byproduct. We validate this approach by comparing with the existing state-of-the-art attention substitute within its own 'Hamburger' framework and by illustrating maps learnt by the module. Overall, this paper serves as a proof of concept for integrating Self-Organising Maps within a supervised network.
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Cambridge Institute of Technology Makes History as the First South Indian Institution to Commend Inaugural Graduates of Samsung Innovation Campus aninews.in - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from aninews.in Daily Mail and Mail on Sunday newspapers.
Machine learning is the ability of an application to identify patterns in the data and predict future events by using these patterns. All data, regardless of type, will have a pattern. Machine learning happens when we train the machine to find and use these patterns to give us some useful predictions.