Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative and Discriminative Voxel Modeling with Convolutional Neural
  Networks   When working with three-dimensional data, choice of representation is key. We
explore voxel-based models, and present evidence for the viability of
voxellated representations in applications including shape modeling and object
classification. Our key contributions are methods for training voxel-based
variational autoencoders, a user interface for exploring the latent space
learned by the autoencoder, and a deep convolutional neural network
architecture for object classification. We address challenges unique to
voxel-based representations, and empirically evaluate our models on the
ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the
state of the art for object classification.

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The categories of this paper are: q-alg
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th