Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture
  Likelihood and Other Modifications   PixelCNNs are a recently proposed class of powerful generative models with
tractable likelihood. Here we discuss our implementation of PixelCNNs which we
make available at https://github.com/openai/pixel-cnn. Our implementation
contains a number of modifications to the original model that both simplify its
structure and improve its performance. 1) We use a discretized logistic mixture
likelihood on the pixels, rather than a 256-way softmax, which we find to speed
up training. 2) We condition on whole pixels, rather than R/G/B sub-pixels,
simplifying the model structure. 3) We use downsampling to efficiently capture
structure at multiple resolutions. 4) We introduce additional short-cut
connections to further speed up optimization. 5) We regularize the model using
dropout. Finally, we present state-of-the-art log likelihood results on
CIFAR-10 to demonstrate the usefulness of these modifications.

25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
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The categories of this paper are: eess
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
2
The categories of this paper are: math
21
The categories of this paper are: adap-org