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Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection


Recent advances in Voice Activity Detection (VAD) are driven by artificial and Recurrent Neural Networks (RNNs), however, using a VAD system in battery-operated devices requires further power efficiency. This can be achieved by neuromorphic hardware, which enables Spiking Neural Networks (SNNs) to perform inference at very low energy consumption. Spiking networks are characterized by their ability to process information efficiently, in a sparse cascade of binary events in time called spikes.

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Authors:
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak
Submitted On:
27 May 2020 - 8:49am
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icassp2020snn_slides.pdf

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[1] Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak, "Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5440. Accessed: Aug. 10, 2020.
@article{5440-20,
url = {http://sigport.org/5440},
author = {Flavio Martinelli; Giorgia Dellaferrera; Pablo Mainar; Milos Cernak },
publisher = {IEEE SigPort},
title = {Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection},
year = {2020} }
TY - EJOUR
T1 - Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection
AU - Flavio Martinelli; Giorgia Dellaferrera; Pablo Mainar; Milos Cernak
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5440
ER -
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak. (2020). Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection. IEEE SigPort. http://sigport.org/5440
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak, 2020. Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection. Available at: http://sigport.org/5440.
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak. (2020). "Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection." Web.
1. Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak. Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5440

Adversarial Networks for Secure Wireless Communications - Slides


We propose a data-driven secure wireless communication scheme, in which the goal is to transmit a signal to a legitimate receiver with minimal distortion, while keeping some information about the signal private from an eavesdropping adversary. When the data distribution is known, the optimal trade-off between the reconstruction quality at the legitimate receiver and the leakage to the adversary can be characterised in the information theoretic asymptotic limit.

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Authors:
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz
Submitted On:
16 May 2020 - 4:33pm
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[1] Thomas Marchioro, Nicola Laurenti, Deniz Gunduz, "Adversarial Networks for Secure Wireless Communications - Slides", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5380. Accessed: Aug. 10, 2020.
@article{5380-20,
url = {http://sigport.org/5380},
author = {Thomas Marchioro; Nicola Laurenti; Deniz Gunduz },
publisher = {IEEE SigPort},
title = {Adversarial Networks for Secure Wireless Communications - Slides},
year = {2020} }
TY - EJOUR
T1 - Adversarial Networks for Secure Wireless Communications - Slides
AU - Thomas Marchioro; Nicola Laurenti; Deniz Gunduz
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5380
ER -
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz. (2020). Adversarial Networks for Secure Wireless Communications - Slides. IEEE SigPort. http://sigport.org/5380
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz, 2020. Adversarial Networks for Secure Wireless Communications - Slides. Available at: http://sigport.org/5380.
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz. (2020). "Adversarial Networks for Secure Wireless Communications - Slides." Web.
1. Thomas Marchioro, Nicola Laurenti, Deniz Gunduz. Adversarial Networks for Secure Wireless Communications - Slides [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5380

Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework


Model-Based Iterative Reconstruction (MBIR) has shown promising results in clinical studies as they allow significant

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Authors:
Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman
Submitted On:
19 April 2018 - 7:12pm
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[1] Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman, "Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3038. Accessed: Aug. 10, 2020.
@article{3038-18,
url = {http://sigport.org/3038},
author = {Dong Hye Ye; Somesh Srivastava; Jean-Baptiste Thibault; Ken Sauer; Charles Bouman },
publisher = {IEEE SigPort},
title = {Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework},
year = {2018} }
TY - EJOUR
T1 - Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework
AU - Dong Hye Ye; Somesh Srivastava; Jean-Baptiste Thibault; Ken Sauer; Charles Bouman
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3038
ER -
Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman. (2018). Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework. IEEE SigPort. http://sigport.org/3038
Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman, 2018. Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework. Available at: http://sigport.org/3038.
Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman. (2018). "Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework." Web.
1. Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman. Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3038