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Recurrent Phase Reconstruction Using Estimated Phase Derivatives from Deep Neural Networks
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
◽
10.1109/icassp39728.2021.9413722
◽
2021
◽
Author(s):
Lars Thieling
◽
Daniel Wilhelm
◽
Peter Jax
Keyword(s):
Neural Networks
◽
Deep Neural Networks
◽
Phase Reconstruction
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Phase Reconstruction Based On Recurrent Phase Unwrapping With Deep Neural Networks
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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10.1109/icassp40776.2020.9053234
◽
2020
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◽
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Yuma Koizumi
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Neural Networks
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Phase reconstruction from amplitude spectrograms based on directional-statistics deep neural networks
Signal Processing
◽
10.1016/j.sigpro.2019.107368
◽
2020
◽
Vol 169
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pp. 107368
◽
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Author(s):
Shinnosuke Takamichi
◽
Yuki Saito
◽
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◽
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◽
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Keyword(s):
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Deep Neural Networks
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Directional Statistics
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Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT
10.32470/ccn.2018.1046-0
◽
2018
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Cited By ~ 1
Author(s):
Alex Hernández-García
◽
Johannes Mehrer
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Nikolaus Kriegeskorte
◽
Peter König
◽
Tim C. Kietzmann
Keyword(s):
Neural Networks
◽
Deep Neural Networks
◽
Data Augmentation
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Representation of adversarial images in deep neural networks and the human brain
10.32470/ccn.2018.1066-0
◽
2018
◽
Author(s):
Chi Zhang
◽
Xiaohan Duan
◽
Ruyuan Zhang
◽
Li Tong
Keyword(s):
Neural Networks
◽
Human Brain
◽
Deep Neural Networks
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Study on Intelligent Security Camera Systems for the Automated Detection of Nighttime Snatching Incidents using Deep Neural Networks
IEEJ Transactions on Industry Applications
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10.1541/ieejias.136.727
◽
2016
◽
Vol 136
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pp. 727-734
◽
Cited By ~ 2
Author(s):
Itaru Nagayama
◽
Akira Miyahara
◽
Koichi Shimabukuro
Keyword(s):
Neural Networks
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Deep Neural Networks
◽
Automated Detection
◽
Camera Systems
◽
Intelligent Security
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Memory Requirement Reduction of Deep Neural Networks for Field Programmable Gate Arrays Using Low-Bit Quantization of Parameters
2020 28th European Signal Processing Conference (EUSIPCO)
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10.23919/eusipco47968.2020.9287739
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2021
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Author(s):
Niccolo Nicodemo
◽
Gaurav Naithani
◽
Konstantinos Drossos
◽
Tuomas Virtanen
◽
Roberto Saletti
Keyword(s):
Neural Networks
◽
Deep Neural Networks
◽
Field Programmable Gate Arrays
◽
Memory Requirement
◽
Gate Arrays
◽
Field Programmable
◽
Programmable Gate Arrays
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The Use of Deep Neural Networks to Predict Post-liver Transplant Mortality
10.26226/morressier.58eb975cd462b80290b4f9e3
◽
2017
◽
Author(s):
Brent Ershoff
Keyword(s):
Neural Networks
◽
Liver Transplant
◽
Deep Neural Networks
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Financial Market Prediction and Improving the Performance Based on Large-scale Exogenous Variables and Deep Neural Networks
Korean Institute of Smart Media
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10.30693/smj.2020.9.4.26
◽
2020
◽
Vol 9
(4)
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pp. 26-35
Author(s):
Sung Gil Cheon
◽
Ju Hong Lee
◽
Bum Ghi Choi
◽
Jae Won Song
Keyword(s):
Neural Networks
◽
Financial Market
◽
Large Scale
◽
Deep Neural Networks
◽
Exogenous Variables
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Faculty Opinions recommendation of Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.
Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature
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10.3410/f.726413891.793534418
◽
2017
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Author(s):
Odelia Schwartz
Keyword(s):
Neural Networks
◽
Object Recognition
◽
Deep Neural Networks
◽
Visual Object
◽
Visual Object Recognition
◽
Cortical Dynamics
◽
Spatio Temporal
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Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks
10.21437/interspeech.2018-1417
◽
2018
◽
Cited By ~ 39
Author(s):
Daniel Povey
◽
Gaofeng Cheng
◽
Yiming Wang
◽
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◽
Hainan Xu
◽
...
Keyword(s):
Neural Networks
◽
Matrix Factorization
◽
Deep Neural Networks
◽
Low Rank
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Rank Matrix
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Low Rank Matrix
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