Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simplicial Embeddings in Self-Supervised Learning and Downstream
  Classification   We introduce Simplicial Embeddings (SEMs) as a way to constrain the encoded
representations of a self-supervised model to $L$ simplices of $V$ dimensions
each using a Softmax operation. This procedure imposes a structure on the
representations that reduce their expressivity for training downstream
classifiers, which helps them generalize better. Specifically, we show that the
temperature $\tau$ of the Softmax operation controls for the SEM
representation's expressivity, allowing us to derive a tighter downstream
classifier generalization bound than that for classifiers using unnormalized
representations. We empirically demonstrate that SEMs considerably improve
generalization on natural image datasets such as CIFAR-100 and ImageNet.
Finally, we also present evidence of the emergence of semantically relevant
features in SEMs, a pattern that is absent from baseline self-supervised
models.

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The categories of this paper are: math
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The categories of this paper are: alg-geom
18
The categories of this paper are: econ
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The categories of this paper are: nlin
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The categories of this paper are: math-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: stat