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[R] Understanding and Controlling Memory in Recurrent Neural Networks (ICML’19 oral)

This paper shows that RNNs are able to form long-term memories despite being trained only for short-term with a limited amount of timesteps, but that not all memories are created equal. The authors find that each memory is correlated with a dynamical object in the hidden-state phase space and that the objects properties can quantitatively predict long term effectiveness. By regularizing the dynamical object, the long-term functionality of the RNN is significantly improved, while not adding to the computational complexity of training.

Link to PDF: http://proceedings.mlr.press/v97/haviv19a/haviv19a.pdf

Oral: Tue Jun 11th 03:10 PM @ Room 201

Poster: Tue Jun 11th 06:30 PM @ Pacific Ballroom #258

submitted by /u/DoronHaviv12
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.