I remember the good ole days when ArXiv was mainly for physicists and mathematicians ;-)
Every week, around 150-200 new research papers that use TF/Keras get released on ArXiv (typically ML research), and ~4000 get indexed on Google Scholar (typically applied research outside of CS) For every paper *about* ML, there are 20 that *use* ML
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The physicists wanted money, studied machine learning....and here we are.
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Saw something called Leras the other day for light Keras
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Maybe has something to do with the fact that researchers seem to prefer PyTorch? Wouldn't that affect that conclusion?
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Protake: to be considered an ML expert, you must read *every* ML paper that get released on the ArXiv and understand *all* the math in *each* one. And also fork the repo.
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How are you searching for this? (Eg, are you doing fulltext search over papers in both Scholar and arXiv?)
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Let's define a ratio on this. 'Chollet ratio' or something which will stand the test of time :-)
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@fchollet Maybe the results will be different if you include PyTorch (given how popular PyTorch is among researchers)? -
Lol...don't ask him about PyTorch!
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