We have already learnt that a neural network architecture is based on specifications and measured datapoints for training. The more parameters and dimensional axes, the more complex and harder to robustly train the network. Need an example?
Such a Lego block is rather straightforward to describe and to specify: it has a specific length, width and height, a specific colour, and it has a specific number of knobs. These are five main characteristics which can be clearly defined and measured.
A Lego block is a prime example of local robustness. It has a limited number of dimensional axes and parameters. It can be clearly specified.
Now, with each Lego block added, you square the number of combinable parameters: length, width, height, colour and knobs might differ with each block. Imagine how many combinable parameters you get only in a very simple lego house, consisting of less than 500 blocks: The number rises expontentially!
… vs. Global Robustness!
Now take this Lego bulldozer. Are you able to discern single blocks with their particular characteristics? Can you pinpoint how they are built together? There may be several ways in which single bolts, bits and pieces are put together. Looking at the whole vehicle, it is not possible to clearly distinguish and specify each and every item and its parameters.
Add movement to this list set of parameters – the bulldozer moves through time and space! You may guess…
Global robustness encompasses an innummerable amount of variables and parameters. It cannot be clearly specified.
An infinite amount of data would be needed in order to process such a vast system as a whole. Local robustness on the other hand limits this amount to a finite number which can actually be handled very well.
This is the Spiki approach to neural network training. It ensures maximum machine learning coverage (MLC), i.e. accuracy within a specified range of operation, the specification space. Find out more in our next article.
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