Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The difference between memory and prediction in linear recurrent
  networks   Recurrent networks are trained to memorize their input better, often in the
hopes that such training will increase the ability of the network to predict.
We show that networks designed to memorize input can be arbitrarily bad at
prediction. We also find, for several types of inputs, that one-node networks
optimized for prediction are nearly at upper bounds on predictive capacity
given by Wiener filters, and are roughly equivalent in performance to randomly
generated five-node networks. Our results suggest that maximizing memory
capacity leads to very different networks than maximizing predictive capacity,
and that optimizing recurrent weights can decrease reservoir size by half an
order of magnitude.

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The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph