scholarly journals Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-Temporal Path Proposals

2017 ◽  
Author(s):  
Yantao Shen ◽  
Tong Xiao ◽  
Hongsheng Li ◽  
Shuai Yi ◽  
Xiaogang Wang
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Spatio Temporal ◽  
Temporal Path

scholarly journals Single Cortical Neurons as Deep Artificial Neural Networks

2019 ◽  
Author(s):  
David Beniaguev ◽  
Idan Segev ◽  
Michael London
Keyword(s):  
Neural Networks ◽  
Cortical Neurons ◽  
Deep Neural Networks ◽  
Pyramidal Neurons ◽  
Classical Architecture ◽  
Novel Approach ◽  
Cortical Pyramidal Neurons ◽  
Spatio Temporal ◽  
Cortical Pyramidal Cell

AbstractWe introduce a novel approach to study neurons as sophisticated I/O information processing units by utilizing recent advances in the field of machine learning. We trained deep neural networks (DNNs) to mimic the I/O behavior of a detailed nonlinear model of a layer 5 cortical pyramidal cell, receiving rich spatio-temporal patterns of input synapse activations. A Temporally Convolutional DNN (TCN) with seven layers was required to accurately, and very efficiently, capture the I/O of this neuron at the millisecond resolution. This complexity primarily arises from local NMDA-based nonlinear dendritic conductances. The weight matrices of the DNN provide new insights into the I/O function of cortical pyramidal neurons, and the approach presented can provide a systematic characterization of the functional complexity of different neuron types. Our results demonstrate that cortical neurons can be conceptualized as multi-layered “deep” processing units, implying that the cortical networks they form have a non-classical architecture and are potentially more computationally powerful than previously assumed.

Modeling Spatio-Temporal Characteristics of Metocean Conditions During Hurricanes Using Deep Neural Networks

2020 ◽  
Author(s):  
Chi Qiao ◽  
Andrew T. Myers
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Dimension Reduction ◽  
Deep Neural Networks ◽  
Principal Component ◽  
Component Analysis ◽  
Offshore Structures ◽  
Complex Nature ◽  
Temporal Characteristics ◽  
Spatio Temporal

Abstract Surrogate modeling of the variability of metocean conditions in space and in time during hurricanes is a crucial task for risk analysis on offshore structures such as offshore wind turbines, which are deployed over a large area. This task is challenging because of the complex nature of the meteorology-metocean interaction in addition to the time-dependence and high-dimensionality of the output. In this paper, spatio-temporal characteristics of surrogate models, such as Deep Neural Networks, are analyzed based on an offshore multi-hazard database created by the authors. The focus of this paper is two-fold: first, the effectiveness of dimension reduction techniques for representing high-dimensional output distributed in space is investigated and, second, an overall approach to estimate spatio-temporal characteristics of hurricane hazards using Deep Neural Networks is presented. The popular dimension reduction technique, Principal Component Analysis, is shown to perform similarly compared to a simpler dimension reduction approach and to not perform as well as a surrogate model implemented without dimension reduction. Discussions are provided to explain why the performance of Principal Component Analysis is only mediocre in this implementation and why dimension reduction might not be necessary.

scholarly journals Human Action Recognition by Learning Spatio-Temporal Features With Deep Neural Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 17913-17922 ◽  
Author(s):  
Lei Wang ◽  
Yangyang Xu ◽  
Jun Cheng ◽  
Haiying Xia ◽  
Jianqin Yin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Action Recognition ◽  
Deep Neural Networks ◽  
Human Action Recognition ◽  
Human Action ◽  
Temporal Features ◽  
Spatio Temporal

Spatio-Temporal Optimization of Deep Neural Networks for Reconfigurable FPGA SoCs

2020 ◽  
pp. 1-1
Author(s):  
Biruk B. Seyoum ◽  
Marco Pagani ◽  
Alessandro Biondi ◽  
Sara Balleri ◽  
Giorgio Buttazzo
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Spatio Temporal
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