Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neuromorphic Acceleration for Approximate Bayesian Inference on Neural
  Networks via Permanent Dropout   As neural networks have begun performing increasingly critical tasks for
society, ranging from driving cars to identifying candidates for drug
development, the value of their ability to perform uncertainty quantification
(UQ) in their predictions has risen commensurately. Permanent dropout, a
popular method for neural network UQ, involves injecting stochasticity into the
inference phase of the model and creating many predictions for each of the test
data. This shifts the computational and energy burden of deep neural networks
from the training phase to the inference phase. Recent work has demonstrated
near-lossless conversion of classical deep neural networks to their spiking
counterparts. We use these results to demonstrate the feasibility of conducting
the inference phase with permanent dropout on spiking neural networks,
mitigating the technique's computational and energy burden, which is essential
for its use at scale or on edge platforms. We demonstrate the proposed approach
via the Nengo spiking neural simulator on a combination drug therapy dataset
for cancer treatment, where UQ is critical. Our results indicate that the
spiking approximation gives a predictive distribution practically
indistinguishable from that given by the classical network.

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The categories of this paper are: hep-ph
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
3
The categories of this paper are: physics