Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalized Bayesian Posterior Expectation Distillation for Deep Neural
  Networks   In this paper, we present a general framework for distilling expectations
with respect to the Bayesian posterior distribution of a deep neural network
classifier, extending prior work on the Bayesian Dark Knowledge framework. The
proposed framework takes as input "teacher" and student model architectures and
a general posterior expectation of interest. The distillation method performs
an online compression of the selected posterior expectation using iteratively
generated Monte Carlo samples. We focus on the posterior predictive
distribution and expected entropy as distillation targets. We investigate
several aspects of this framework including the impact of uncertainty and the
choice of student model architecture. We study methods for student model
architecture search from a speed-storage-accuracy perspective and evaluate
down-stream tasks leveraging entropy distillation including uncertainty ranking
and out-of-distribution detection.

25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga