kirk86:

I suppose that means you tried on two superimposed sine waves?, that’s important cause it provides more structure for the network to fit. But if it didn’t work then that’s sketchy, are you replicating the same net architecture and params as in the paper, discliamer haven’t read the full paper in detail so I dunno what net they used to produce Figure 2.


Yup, that’s on the superposition. I skimmed through the paper, but wasn’t able to find any architecture or params related to the network used in producing Figure. 2.



 kirk86:

I don’t suppose that would work in reality, what I mean is usually we don’t know the underlying structure of data that’s one of the main reasons we use these nets. We usually hope to approximate the data as close as possible but if we know in advance our problem then we could use that knowledge.


That does make sense. Even playing around with the superimposed signal, increasing the number of data points, increases the time it needed to learn the entire scope of the function.