Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Universally Quantized Neural Compression   A popular approach to learning encoders for lossy compression is to use
additive uniform noise during training as a differentiable approximation to
test-time quantization. We demonstrate that a uniform noise channel can also be
implemented at test time using universal quantization (Ziv, 1985). This allows
us to eliminate the mismatch between training and test phases while maintaining
a completely differentiable loss function. Implementing the uniform noise
channel is a special case of the more general problem of communicating a
sample, which we prove is computationally hard if we do not make assumptions
about its distribution. However, the uniform special case is efficient as well
as easy to implement and thus of great interest from a practical point of view.
Finally, we show that quantization can be obtained as a limiting case of a soft
quantizer applied to the uniform noise channel, bridging compression with and
without quantization.

10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas