Pedestrian Detection based on Reduced High-Dimensional Distinctive Feature using Deep Neural Network

2018 ◽  
Author(s):  
Hyun Chul Song ◽  
Kwang Nam Choi
Keyword(s):  
Neural Network ◽  
Distinctive Feature ◽  
Deep Neural Network ◽  
Pedestrian Detection ◽  
High Dimensional

scholarly journals A Deep Neural Network Algorithm for Semilinear Elliptic PDEs with Applications in Insurance Mathematics

Risks ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 136
Author(s):  
Stefan Kremsner ◽  
Alexander Steinicke ◽  
Michaela Szölgyenyi
Keyword(s):  
Neural Network ◽  
Partial Differential Equations ◽  
Differential Equations ◽  
Deep Neural Network ◽  
Risk Measure ◽  
Elliptic Partial Differential Equations ◽  
High Dimensional ◽  
Control Problems ◽  
Partial Differential ◽  
Neural Network Algorithm

In insurance mathematics, optimal control problems over an infinite time horizon arise when computing risk measures. An example of such a risk measure is the expected discounted future dividend payments. In models which take multiple economic factors into account, this problem is high-dimensional. The solutions to such control problems correspond to solutions of deterministic semilinear (degenerate) elliptic partial differential equations. In the present paper we propose a novel deep neural network algorithm for solving such partial differential equations in high dimensions in order to be able to compute the proposed risk measure in a complex high-dimensional economic environment. The method is based on the correspondence of elliptic partial differential equations to backward stochastic differential equations with unbounded random terminal time. In particular, backward stochastic differential equations—which can be identified with solutions of elliptic partial differential equations—are approximated by means of deep neural networks.

scholarly journals Performance-optimized neural networks as an explanatory framework for decision confidence

2021 ◽  
Author(s):  
Taylor W Webb ◽  
Kiyofumi Miyoshi ◽  
Tsz Yan So ◽  
Sivananda Rajananda ◽  
Hakwan Lau
Keyword(s):  
Neural Network ◽  
Signal Detection Theory ◽  
Deep Neural Network ◽  
High Dimensional ◽  
Decision Variable ◽  
Sensory Inputs ◽  
Decision Confidence ◽  
Dimensional Modeling ◽  
Principled Explanation ◽  
Low Dimensional

Previous work has sought to understand decision confidence as a prediction of the probability that a decision will be correct, leading to debate over whether these predictions are optimal, and whether they rely on the same decision variable as decisions themselves. This work has generally relied on idealized, low-dimensional modeling frameworks, such as signal detection theory or Bayesian inference, leaving open the question of how decision confidence operates in the domain of high-dimensional, naturalistic stimuli. To address this, we developed a deep neural network model optimized to assess decision confidence directly given high-dimensional inputs such as images. The model naturally accounts for a number of puzzling dissociations between decisions and confidence, suggests a principled explanation of these dissociations in terms of optimization for the statistics of sensory inputs, and makes the surprising prediction that, despite these dissociations, decisions and confidence depend on a common decision variable.

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