Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Efficient Uncertainty Estimation for Resource-Constrained Mobile
  Applications   Deep neural networks have shown great success in prediction quality while
reliable and robust uncertainty estimation remains a challenge. Predictive
uncertainty supplements model predictions and enables improved functionality of
downstream tasks including embedded and mobile applications, such as virtual
reality, augmented reality, sensor fusion, and perception. These applications
often require a compromise in complexity to obtain uncertainty estimates due to
very limited memory and compute resources. We tackle this problem by building
upon Monte Carlo Dropout (MCDO) models using the Axolotl framework;
specifically, we diversify sampled subnetworks, leverage dropout patterns, and
use a branching technique to improve predictive performance while maintaining
fast computations. We conduct experiments on (1) a multi-class classification
task using the CIFAR10 dataset, and (2) a more complex human body segmentation
task. Our results show the effectiveness of our approach by reaching close to
Deep Ensemble prediction quality and uncertainty estimation, while still
achieving faster inference on resource-limited mobile platforms.

22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
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
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th