Authors
Łukasz Kaiser, Ilya Sutskever
Publication date
2015/11/25
Journal
arXiv preprint arXiv:1511.08228
Description
Abstract: Learning an algorithm from examples is a fundamental problem that has been
widely studied. Recently it has been addressed using neural networks, in particular by
Neural Turing Machines (NTMs). These are fully differentiable computers that use
backpropagation to learn their own programming. Despite their appeal NTMs have a
weakness that is caused by their sequential nature: they cannot be parallelized and are are
hard to train due to their large depth when unfolded.
Total citations
20152016315
Scholar articles
Ł Kaiser, I Sutskever - arXiv preprint arXiv:1511.08228, 2015