Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cumulative distribution networks and the derivative-sum-product
  algorithm   We introduce a new type of graphical model called a "cumulative distribution
network" (CDN), which expresses a joint cumulative distribution as a product of
local functions. Each local function can be viewed as providing evidence about
possible orderings, or rankings, of variables. Interestingly, we find that the
conditional independence properties of CDNs are quite different from other
graphical models. We also describe a messagepassing algorithm that efficiently
computes conditional cumulative distributions. Due to the unique independence
properties of the CDN, these messages do not in general have a one-to-one
correspondence with messages exchanged in standard algorithms, such as belief
propagation. We demonstrate the application of CDNs for structured ranking
learning using a previously-studied multi-player gaming dataset.

4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex