My impression on DL papers is that they are relatively clear to other papers in machine learning/math/stats. Especially for big conferences, many of the accepted papers have code released on github before the conference date either by the original researchers or by developers looking to get stars (usually googling paper and + github will help you here). A lot of these implementations end up turning into blog posts or being contributed to Theano/TensorFlow/Torch/Caffe/etc.
I don't see a huge competitive advantage in not releasing code. It's probably more beneficial to share ideas and collaborate. I imagine the reason it's not done more often than it is is because it takes time and effort to put this stuff out there.
I don't see a huge competitive advantage in not releasing code. It's probably more beneficial to share ideas and collaborate. I imagine the reason it's not done more often than it is is because it takes time and effort to put this stuff out there.