Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variational Multi-Task Learning with Gumbel-Softmax Priors   Multi-task learning aims to explore task relatedness to improve individual
tasks, which is of particular significance in the challenging scenario that
only limited data is available for each task. To tackle this challenge, we
propose variational multi-task learning (VMTL), a general probabilistic
inference framework for learning multiple related tasks. We cast multi-task
learning as a variational Bayesian inference problem, in which task relatedness
is explored in a unified manner by specifying priors. To incorporate shared
knowledge into each task, we design the prior of a task to be a learnable
mixture of the variational posteriors of other related tasks, which is learned
by the Gumbel-Softmax technique. In contrast to previous methods, our VMTL can
exploit task relatedness for both representations and classifiers in a
principled way by jointly inferring their posteriors. This enables individual
tasks to fully leverage inductive biases provided by related tasks, therefore
improving the overall performance of all tasks. Experimental results
demonstrate that the proposed VMTL is able to effectively tackle a variety of
challenging multi-task learning settings with limited training data for both
classification and regression. Our method consistently surpasses previous
methods, including strong Bayesian approaches, and achieves state-of-the-art
performance on five benchmark datasets.

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The categories of this paper are: math
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom