1,825 karma · joined October 10, 2012
email : harsh.it[at]yandex.com
I have true hate for the authors of this paper: "A deep learning framework for financial time series using stacked autoencoders and long-short term memory". Probably the most complex AND vague in terms of methodology and after weeks trying to reproduce their results (and failing) I figured out that they were leaking future data into their training set (this also happens more than you'd think)."
- Not sure how author tried to implement it , but is this not how you train LSTM networks by feeding t+1 data back into the cell again to predict t+2 data. It will be easier if author made it open source as well
Edit: https://medium.com/tensorflow/an-introduction-to-probabilist... There is support in tensorflow for probabilistic programming. How is this any different?
Whats wrong with a fersh-faced grad student trying to build a new feature if at all he/she is trying to do so?
I don't agree with this statement, a simple look at cs231n lecture series will show you how much math is involved. A lot of articles/people claim its a black box, but while writing a small network architecture you realise it's not. Methods like stride, padding, activations, learning rate, optimisations, drop outs etc. give you "aha" moment which is followed by a mathematical explanation. One should study the topic thoroughly before criticising it.
I fail to understand if it's their duty to inculcate critical thinking in all of us . While reading any article on websites I try to fact check while searching on google , I dont believe any random article getting viral. Yes I agree there is lot of content online which might not be correct but outsourcing critical thinking to computers using correct resources might not be a bad idea depending on way it is done