Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Molecular Graph Neural Network Explainability with
  Orthonormalization and Induced Sparsity   Rationalizing which parts of a molecule drive the predictions of a molecular
graph convolutional neural network (GCNN) can be difficult. To help, we propose
two simple regularization techniques to apply during the training of GCNNs:
Batch Representation Orthonormalization (BRO) and Gini regularization. BRO,
inspired by molecular orbital theory, encourages graph convolution operations
to generate orthonormal node embeddings. Gini regularization is applied to the
weights of the output layer and constrains the number of dimensions the model
can use to make predictions. We show that Gini and BRO regularization can
improve the accuracy of state-of-the-art GCNN attribution methods on artificial
benchmark datasets. In a real-world setting, we demonstrate that medicinal
chemists significantly prefer explanations extracted from regularized models.
While we only study these regularizers in the context of GCNNs, both can be
applied to other types of neural networks

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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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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: quant-ph
31
The categories of this paper are: ao-sci
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
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The categories of this paper are: hep-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph