Deep Learning for AI
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注目のコメント
"We believe that deep networks excel because they exploit a particular form of compositionality in which features in one layer are combined in many different ways to create more abstract features in the next layer."
"This is a transformative addition to the neural net toolbox, in that it changes neural nets from purely vector transformation machines into architectures which can dynamically choose which inputs they operate on, and can store information in differentiable associative memories. A key property of such architectures is that they can effectively operate on different kinds of data structures including sets and graphs."
"Using deep learning for system 2 tasks that require a deliberate sequence of steps is an exciting area that is still in its infancy."
"The nature of system 2 processing and cognitive neuroscience theories for them5,30 suggests several such inductive biases and architectures,11,45 which may be exploited to design novel deep learning systems. "
