Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Geometric Disentanglement by Random Convex Polytopes   We propose a new geometric method for measuring the quality of
representations obtained from deep learning. Our approach, called Random
Polytope Descriptor, provides an efficient description of data points based on
the construction of random convex polytopes. We demonstrate the use of our
technique by qualitatively comparing the behavior of classic and regularized
autoencoders. This reveals that applying regularization to autoencoder networks
may decrease the out-of-distribution detection performance in latent space.
While our technique is similar in spirit to $k$-means clustering, we achieve
significantly better false positive/negative balance in clustering tasks on
autoencoded datasets.

10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th