Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms — mentioned by hcburger

Wed, 06 Apr 2016 14:15:12 GMT

I'm the author of the blog post. I think you're right in saying that neural nets are hard to reason about. It's probably impossible to put into words exactly how a deep neural network achieves its results. There are two ways to interpret this: 1) This is a bad thing, because we don't understand exactly how the neural net works. We can't really be sure there won't be an unexpected failure case. 2) This is a good thing, because we don't HAVE to understand how the neural network works. We had no idea how to solve the problem, but we just threw loads of data at a neural network, and it solved the…

Paper and discussion history. A mention is not an endorsement.

Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms · HackerPapers discussions

Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms — mentioned by hcburger