title: Learning to Remember Rare Events
abstract: Despite recent advances, memory-augmented deep neural networks are still limited
  when it comes to life-long and one-shot learning, especially in remembering rare events.
  We present a large-scale life-long memory module for use in deep learning.
  The module exploits fast nearest-neighbor algorithms for efficiency and
  thus scales to large memory sizes.
  Except for the nearest-neighbor query, the module is fully differentiable
  and trained end-to-end with no extra supervision.  It operates in
  a life-long manner, i.e., without the need to reset it during training.
  
  Our memory module can be easily added to any part of a supervised neural network.
  To show its versatility we add it to a number of networks, from simple
  convolutional ones tested on image classification to deep sequence-to-sequence
  and recurrent-convolutional models.
  In all cases, the enhanced network gains the ability to remember
  and do life-long one-shot learning.
  Our module remembers training examples shown many thousands
  of steps in the past and it can successfully generalize from them.
  We set new state-of-the-art for one-shot learning on the Omniglot dataset
  and demonstrate, for the first time, life-long one-shot learning in
  recurrent neural networks on a large-scale machine translation task.
authors: Lukasz Kaiser Ofir Nachum Aurko Roy Samy Bengio
hIndex: Samy Bengio 83