Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Molecule Property Prediction and Classification with Graph Hypernetworks   Graph neural networks are currently leading the performance charts in
learning-based molecule property prediction and classification. Computational
chemistry has, therefore, become the a prominent testbed for generic graph
neural networks, as well as for specialized message passing methods. In this
work, we demonstrate that the replacement of the underlying networks with
hypernetworks leads to a boost in performance, obtaining state of the art
results in various benchmarks. A major difficulty in the application of
hypernetworks is their lack of stability. We tackle this by combining the
current message and the first message. A recent work has tackled the training
instability of hypernetworks in the context of error correcting codes, by
replacing the activation function of the message passing network with a
low-order Taylor approximation of it. We demonstrate that our generic solution
can replace this domain-specific solution.

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The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
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The categories of this paper are: supr-con
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-ex