| Overconfident pronouncement by indicating that they are making computational models of natural processes that no one can confidently state are correct or are the most efficient? Making statements that allow people to see behind the curtains and maybe go off and make their own competitive models... Yes, this is a disservice to the advancement of A.I and should be downvoted : Removing the prestigious veil and illusion from published works. NTMs memory is external in what sense? Please detail what this means in a 'functional' sense. It's biologically inspired. Neurons maintain memory beyond synaptic weights. The neuron models of present day A.I were basic. Someone comes along and sees the obvious : There is no computational model for how neurons utilize memory and suddenly they're thinking on another level? Give me a break.. Synthetic gradients have everything to do w/ electro-chemical gradients :
http://www.nature.com/articles/srep14527
http://www.pnas.org/content/110/30/12456.full.pdf
So, where is your establishment that I am incorrect. It is nowhere to be found. Again, biologically inspired computational models. Oh look, someone published a paper back in June that is an implementation of Differentiable Neural Computers:
https://arxiv.org/abs/1607.00036 It's hype and that is a disservice to the community of people completing similar work and taking similar approaches. It would be an interesting discussion to have. That discussion was terminated in favor of downvoting me. They're feeble to someone who isn't well informed on neuroscience. Thus, you'd rather be wow'd and believe in the fantasy that only a small segment of people can write computational models of biology. Continue believing the hype. Rarely will someone be truthful and honest about where they got their ideas when hype follows. An interesting conversation could have transpired. Enjoy the feels from the downvotes. |
An Synthetic Gradient is a way to allow learning Forward Propagated Neural Nets in a parallel way. The gradient here is referring to the 'error' backpropagation that is part of the training process of an neural net (im talking about computer science neural nets).
They have nothing todo with each other. The papers that you are referring to have nothing todo with the process of training a neural net.