Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multitask Hopfield Networks   Multitask algorithms typically use task similarity information as a bias to
speed up and improve the performance of learning processes. Tasks are learned
jointly, sharing information across them, in order to construct models more
accurate than those learned separately over single tasks. In this contribution,
we present the first multitask model, to our knowledge, based on Hopfield
Networks (HNs), named HoMTask. We show that by appropriately building a unique
HN embedding all tasks, a more robust and effective classification model can be
learned. HoMTask is a transductive semi-supervised parametric HN, that
minimizes an energy function extended to all nodes and to all tasks under
study. We provide theoretical evidence that the optimal parameters
automatically estimated by HoMTask make coherent the model itself with the
prior knowledge (connection weights and node labels). The convergence
properties of HNs are preserved, and the fixed point reached by the network
dynamics gives rise to the prediction of unlabeled nodes. The proposed model
improves the classification abilities of singletask HNs on a preliminary
benchmark comparison, and achieves competitive performance with
state-of-the-art semi-supervised graph-based algorithms.

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The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
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The categories of this paper are: nucl-ex
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: cond-mat
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The categories of this paper are: plasm-ph
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The categories of this paper are: physics