Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Invariant Weights in Neural Networks   Assumptions about invariances or symmetries in data can significantly
increase the predictive power of statistical models. Many commonly used models
in machine learning are constraint to respect certain symmetries in the data,
such as translation equivariance in convolutional neural networks, and
incorporation of new symmetry types is actively being studied. Yet, efforts to
learn such invariances from the data itself remains an open research problem.
It has been shown that marginal likelihood offers a principled way to learn
invariances in Gaussian Processes. We propose a weight-space equivalent to this
approach, by minimizing a lower bound on the marginal likelihood to learn
invariances in neural networks resulting in naturally higher performing models.

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The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
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The categories of this paper are: eess
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The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
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The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph