Why not use conda?
...is $1.5M.
Seems like in that case you would train both models separately on different cost functions. By phrasing it as a layer I was expecting both the SVM and the DNN could be trained simultaneously.
Could you explain in a bit more detail how you would integrate an SVM layer into a DNN? The kernel matrix depends on all samples, while at training time you would only have access to those in the minibatch.
I think he is actually a she.
The premise of the optimizations in this article don't always hold, unfortunately. If x is floating point, sum(reverse(x)) is unlikely to be equal to sum(x) for long enough x, and sorting x on beforehand will make this…
Why not use conda?
...is $1.5M.
Seems like in that case you would train both models separately on different cost functions. By phrasing it as a layer I was expecting both the SVM and the DNN could be trained simultaneously.
Could you explain in a bit more detail how you would integrate an SVM layer into a DNN? The kernel matrix depends on all samples, while at training time you would only have access to those in the minibatch.
I think he is actually a she.
The premise of the optimizations in this article don't always hold, unfortunately. If x is floating point, sum(reverse(x)) is unlikely to be equal to sum(x) for long enough x, and sorting x on beforehand will make this…