Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiset-Equivariant Set Prediction with Approximate Implicit
  Differentiation   Most set prediction models in deep learning use set-equivariant operations,
but they actually operate on multisets. We show that set-equivariant functions
cannot represent certain functions on multisets, so we introduce the more
appropriate notion of multiset-equivariance. We identify that the existing Deep
Set Prediction Network (DSPN) can be multiset-equivariant without being
hindered by set-equivariance and improve it with approximate implicit
differentiation, allowing for better optimization while being faster and saving
memory. In a range of toy experiments, we show that the perspective of
multiset-equivariance is beneficial and that our changes to DSPN achieve better
results in most cases. On CLEVR object property prediction, we substantially
improve over the state-of-the-art Slot Attention from 8% to 77% in one of the
strictest evaluation metrics because of the benefits made possible by implicit
differentiation.

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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: chem-ph
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The categories of this paper are: q-alg