> Seeing different ways to look at something is always refreshing ... how do YOU think about NNs? A year or two ago, when I was really doing the wandering res

> Seeing different ways to look at something is always refreshing ... how do YOU think about NNs? A year or two ago, when I was really doing the wandering researcher thing, I made a point of asking lots of people at different groups how they saw deep learning. I think these three narratives capture most of what I heard, although a people blended them in different ways. Certain views are more common in particular groups. For example, people at the Montreal group seem more inclined to think about things from the representations narrative than other groups. (Although, I think I have just about everyone else beat in my extreme version of seeing everything from the manifold perspective. :P ) I feel like there's some pretty interesting quasi-sociological work to be done on deep learning. Because the community has grown so fast, and there's a lot of variety in how people think. >> ... chain of composed functions is optimized.. > This is certainly true for the majority of models in use today, but I wonder if this will remain true for future architectures. I suspect there's something very fundamental about chains of composed functions. I can't formalize my feeling into a strong argument though. > In the case of a network that is supposed to update it's own weights during normal operation (i.e. topic discovery), the functional narrative is less clear to me. I don't think matters too much. The functional graph becomes more complicated, but it's just a matter of the output of one function going to multiple places.