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Neural network learning (MLR-NNLR)

WET-DRY CLASSIFICATION USING LSTM AND COMMERCIALMICROWAVE LINKS


The task of rain detection, or wet-dry classification
using measurements from commercial microwave links (CMLs)
is a subject that been studied in depth. However, these studies
are based on direct measurement of the signal level, which
is known to be attenuated by rain. In this paper we present,
for the first time an empirical study on rain classification using
records of transmissions errors in the CMLs. Based on a dataset
of measurements taken from operational cellular backhaul
networks and meteorological measurements, and using long

Paper Details

Authors:
Hagit Messer
Submitted On:
2 July 2018 - 11:34am
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poster-sam2018.pdf

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[1] Hagit Messer, "WET-DRY CLASSIFICATION USING LSTM AND COMMERCIALMICROWAVE LINKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3349. Accessed: Aug. 21, 2018.
@article{3349-18,
url = {http://sigport.org/3349},
author = {Hagit Messer },
publisher = {IEEE SigPort},
title = {WET-DRY CLASSIFICATION USING LSTM AND COMMERCIALMICROWAVE LINKS},
year = {2018} }
TY - EJOUR
T1 - WET-DRY CLASSIFICATION USING LSTM AND COMMERCIALMICROWAVE LINKS
AU - Hagit Messer
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3349
ER -
Hagit Messer. (2018). WET-DRY CLASSIFICATION USING LSTM AND COMMERCIALMICROWAVE LINKS. IEEE SigPort. http://sigport.org/3349
Hagit Messer, 2018. WET-DRY CLASSIFICATION USING LSTM AND COMMERCIALMICROWAVE LINKS. Available at: http://sigport.org/3349.
Hagit Messer. (2018). "WET-DRY CLASSIFICATION USING LSTM AND COMMERCIALMICROWAVE LINKS." Web.
1. Hagit Messer. WET-DRY CLASSIFICATION USING LSTM AND COMMERCIALMICROWAVE LINKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3349

Learning-Based Antenna Selection for Multicasting


In multi-antenna systems, it is preferred to activate only a subset of the available transmit antennas in order to save hardware and energy resources, without seriously degrading the system performance. However, antenna selection often poses very hard optimization problems. Joint multicast beamforming and antenna selection is one particular example, which is often approached by Semi-Definite Relaxation (SDR) type approximations. The drawback is that SDR lifts the problem to a much higher dimension, leading to considerably high memory and computational complexities.

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Authors:
Mohamed S. Ibrahim, Ahmed S. Zamzam, Xiao Fu, Nicholas D. Sidiropoulos
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24 June 2018 - 12:17pm
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NN_Poster_SP18.pdf

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[1] Mohamed S. Ibrahim, Ahmed S. Zamzam, Xiao Fu, Nicholas D. Sidiropoulos, "Learning-Based Antenna Selection for Multicasting", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3322. Accessed: Aug. 21, 2018.
@article{3322-18,
url = {http://sigport.org/3322},
author = {Mohamed S. Ibrahim; Ahmed S. Zamzam; Xiao Fu; Nicholas D. Sidiropoulos },
publisher = {IEEE SigPort},
title = {Learning-Based Antenna Selection for Multicasting},
year = {2018} }
TY - EJOUR
T1 - Learning-Based Antenna Selection for Multicasting
AU - Mohamed S. Ibrahim; Ahmed S. Zamzam; Xiao Fu; Nicholas D. Sidiropoulos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3322
ER -
Mohamed S. Ibrahim, Ahmed S. Zamzam, Xiao Fu, Nicholas D. Sidiropoulos. (2018). Learning-Based Antenna Selection for Multicasting. IEEE SigPort. http://sigport.org/3322
Mohamed S. Ibrahim, Ahmed S. Zamzam, Xiao Fu, Nicholas D. Sidiropoulos, 2018. Learning-Based Antenna Selection for Multicasting. Available at: http://sigport.org/3322.
Mohamed S. Ibrahim, Ahmed S. Zamzam, Xiao Fu, Nicholas D. Sidiropoulos. (2018). "Learning-Based Antenna Selection for Multicasting." Web.
1. Mohamed S. Ibrahim, Ahmed S. Zamzam, Xiao Fu, Nicholas D. Sidiropoulos. Learning-Based Antenna Selection for Multicasting [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3322

Time Series Prediction via Recurrent Neural Networks with the Information Bottleneck Principle

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21 June 2018 - 3:25am
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[1] , "Time Series Prediction via Recurrent Neural Networks with the Information Bottleneck Principle", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3277. Accessed: Aug. 21, 2018.
@article{3277-18,
url = {http://sigport.org/3277},
author = { },
publisher = {IEEE SigPort},
title = {Time Series Prediction via Recurrent Neural Networks with the Information Bottleneck Principle},
year = {2018} }
TY - EJOUR
T1 - Time Series Prediction via Recurrent Neural Networks with the Information Bottleneck Principle
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3277
ER -
. (2018). Time Series Prediction via Recurrent Neural Networks with the Information Bottleneck Principle. IEEE SigPort. http://sigport.org/3277
, 2018. Time Series Prediction via Recurrent Neural Networks with the Information Bottleneck Principle. Available at: http://sigport.org/3277.
. (2018). "Time Series Prediction via Recurrent Neural Networks with the Information Bottleneck Principle." Web.
1. . Time Series Prediction via Recurrent Neural Networks with the Information Bottleneck Principle [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3277

PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING


We apply convolutional neural networks (CNN) for monitoring the
operation of photovoltaic panels. In particular, we predict the daily
electrical power curve of a photovoltaic panel based on the power
curves of neighboring panels. An exceptionally large deviation between
predicted and actual (observed) power curve indicates a malfunctioning
panel. The problem is challenging because the power
curve depends on many factors such as weather conditions and the
surrounding objects causing shadows with a regular time pattern. We

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1 June 2018 - 8:12am
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huuhtanen01.pdf

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[1] , "PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3228. Accessed: Aug. 21, 2018.
@article{3228-18,
url = {http://sigport.org/3228},
author = { },
publisher = {IEEE SigPort},
title = {PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING},
year = {2018} }
TY - EJOUR
T1 - PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3228
ER -
. (2018). PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING. IEEE SigPort. http://sigport.org/3228
, 2018. PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING. Available at: http://sigport.org/3228.
. (2018). "PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING." Web.
1. . PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3228

Convolutional Neural Networks via Node-Varying Graph Filters


Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks. Since two of the key operations that CNNs implement are convolution and pooling, this type of networks is implicitly designed to act on data described by regular structures such as images. Motivated by the recent interest in processing signals defined in irregular domains, we advocate a CNN architecture that operates on signals supported on graphs.

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Authors:
Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro
Submitted On:
31 May 2018 - 7:03pm
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gama-leus-marques-ribeiro-node_variant_graph_filter.pdf

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[1] Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro, "Convolutional Neural Networks via Node-Varying Graph Filters", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3226. Accessed: Aug. 21, 2018.
@article{3226-18,
url = {http://sigport.org/3226},
author = {Fernando Gama; Geert Leus; Antonio Marques; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Convolutional Neural Networks via Node-Varying Graph Filters},
year = {2018} }
TY - EJOUR
T1 - Convolutional Neural Networks via Node-Varying Graph Filters
AU - Fernando Gama; Geert Leus; Antonio Marques; Alejandro Ribeiro
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3226
ER -
Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro. (2018). Convolutional Neural Networks via Node-Varying Graph Filters. IEEE SigPort. http://sigport.org/3226
Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro, 2018. Convolutional Neural Networks via Node-Varying Graph Filters. Available at: http://sigport.org/3226.
Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro. (2018). "Convolutional Neural Networks via Node-Varying Graph Filters." Web.
1. Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro. Convolutional Neural Networks via Node-Varying Graph Filters [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3226

Deep CNN Sparse Coding Analysis


Deep Convolutional Sparse Coding (D-CSC) is a framework reminiscent
of deep convolutional neural nets (DCNN), but by omitting the learning of the
dictionaries one can more transparently analyse the role of the
activation function and its ability to recover activation paths
through the layers. Papyan, Romano, and Elad conducted an analysis of
such an architecture \cite{2016arXiv160708194P}, demonstrated the
relationship with DCNNs and proved conditions under which a D-CSC is
guaranteed to recover activation paths. A technical innovation of

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Authors:
Michael Murray, Jared Tanner
Submitted On:
31 May 2018 - 12:05pm
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Deep CNN Sparse Coding Analysis.pdf

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[1] Michael Murray, Jared Tanner, "Deep CNN Sparse Coding Analysis", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3225. Accessed: Aug. 21, 2018.
@article{3225-18,
url = {http://sigport.org/3225},
author = {Michael Murray; Jared Tanner },
publisher = {IEEE SigPort},
title = {Deep CNN Sparse Coding Analysis},
year = {2018} }
TY - EJOUR
T1 - Deep CNN Sparse Coding Analysis
AU - Michael Murray; Jared Tanner
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3225
ER -
Michael Murray, Jared Tanner. (2018). Deep CNN Sparse Coding Analysis. IEEE SigPort. http://sigport.org/3225
Michael Murray, Jared Tanner, 2018. Deep CNN Sparse Coding Analysis. Available at: http://sigport.org/3225.
Michael Murray, Jared Tanner. (2018). "Deep CNN Sparse Coding Analysis." Web.
1. Michael Murray, Jared Tanner. Deep CNN Sparse Coding Analysis [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3225

MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS


Deep learning on graphs and in particular, graph convolutional neural networks, have recently attracted significant attention in the machine learning community. Many of such

MotifNet.pdf

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Authors:
Federico Monti, Karl Otness, Michael M. Bronstein
Submitted On:
31 May 2018 - 10:30am
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MotifNet.pdf

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[1] Federico Monti, Karl Otness, Michael M. Bronstein, "MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3224. Accessed: Aug. 21, 2018.
@article{3224-18,
url = {http://sigport.org/3224},
author = {Federico Monti; Karl Otness; Michael M. Bronstein },
publisher = {IEEE SigPort},
title = {MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS},
year = {2018} }
TY - EJOUR
T1 - MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS
AU - Federico Monti; Karl Otness; Michael M. Bronstein
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3224
ER -
Federico Monti, Karl Otness, Michael M. Bronstein. (2018). MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS. IEEE SigPort. http://sigport.org/3224
Federico Monti, Karl Otness, Michael M. Bronstein, 2018. MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS. Available at: http://sigport.org/3224.
Federico Monti, Karl Otness, Michael M. Bronstein. (2018). "MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS." Web.
1. Federico Monti, Karl Otness, Michael M. Bronstein. MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3224

INVESTIGATING LABEL NOISE SENSITIVITY OF CONVOLUTIONAL NEURAL NETWORKS FOR FINE GRAINED AUDIO SIGNAL LABELLING


We measure the effect of small amounts of systematic and
random label noise caused by slightly misaligned ground truth
labels in a fine grained audio signal labeling task. The task
we choose to demonstrate these effects on is also known as
framewise polyphonic transcription or note quantized multi-
f0 estimation, and transforms a monaural audio signal into a
sequence of note indicator labels. It will be shown that even
slight misalignments have clearly apparent effects, demonstrating a great sensitivity of convolutional neural networks

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Submitted On:
23 April 2018 - 9:01pm
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[1] , "INVESTIGATING LABEL NOISE SENSITIVITY OF CONVOLUTIONAL NEURAL NETWORKS FOR FINE GRAINED AUDIO SIGNAL LABELLING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3156. Accessed: Aug. 21, 2018.
@article{3156-18,
url = {http://sigport.org/3156},
author = { },
publisher = {IEEE SigPort},
title = {INVESTIGATING LABEL NOISE SENSITIVITY OF CONVOLUTIONAL NEURAL NETWORKS FOR FINE GRAINED AUDIO SIGNAL LABELLING},
year = {2018} }
TY - EJOUR
T1 - INVESTIGATING LABEL NOISE SENSITIVITY OF CONVOLUTIONAL NEURAL NETWORKS FOR FINE GRAINED AUDIO SIGNAL LABELLING
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3156
ER -
. (2018). INVESTIGATING LABEL NOISE SENSITIVITY OF CONVOLUTIONAL NEURAL NETWORKS FOR FINE GRAINED AUDIO SIGNAL LABELLING. IEEE SigPort. http://sigport.org/3156
, 2018. INVESTIGATING LABEL NOISE SENSITIVITY OF CONVOLUTIONAL NEURAL NETWORKS FOR FINE GRAINED AUDIO SIGNAL LABELLING. Available at: http://sigport.org/3156.
. (2018). "INVESTIGATING LABEL NOISE SENSITIVITY OF CONVOLUTIONAL NEURAL NETWORKS FOR FINE GRAINED AUDIO SIGNAL LABELLING." Web.
1. . INVESTIGATING LABEL NOISE SENSITIVITY OF CONVOLUTIONAL NEURAL NETWORKS FOR FINE GRAINED AUDIO SIGNAL LABELLING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3156

VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning


In this paper, we propose a novel virtual reality image quality assessment (VR IQA) with adversarial learning for omnidirectional images. To take into account the characteristics of the omnidirectional image, we devise deep networks including novel quality score predictor and human perception guider. The proposed quality score predictor automatically predicts the quality score of distorted image using the latent spatial and position feature.

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Authors:
Heoun-taek Lim, Hak Gu Kim, and Yong Man Ro
Submitted On:
20 April 2018 - 8:00am
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VR IQA NET-ICASSP2018

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[1] Heoun-taek Lim, Hak Gu Kim, and Yong Man Ro, "VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3102. Accessed: Aug. 21, 2018.
@article{3102-18,
url = {http://sigport.org/3102},
author = {Heoun-taek Lim; Hak Gu Kim; and Yong Man Ro },
publisher = {IEEE SigPort},
title = {VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning},
year = {2018} }
TY - EJOUR
T1 - VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning
AU - Heoun-taek Lim; Hak Gu Kim; and Yong Man Ro
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3102
ER -
Heoun-taek Lim, Hak Gu Kim, and Yong Man Ro. (2018). VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning. IEEE SigPort. http://sigport.org/3102
Heoun-taek Lim, Hak Gu Kim, and Yong Man Ro, 2018. VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning. Available at: http://sigport.org/3102.
Heoun-taek Lim, Hak Gu Kim, and Yong Man Ro. (2018). "VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning." Web.
1. Heoun-taek Lim, Hak Gu Kim, and Yong Man Ro. VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3102

TasNet: time-domain audio separation network for real-time, single-channel speech separation


Robust speech processing in multi-talker environments requires effective speech separation. Recent deep learning systems have made significant progress toward solving this problem, yet it remains challenging particularly in real-time, short latency applications. Most methods attempt to construct a mask for each source in time-frequency representation of the mixture signal which is not necessarily an optimal representation for speech separation.

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Authors:
Yi Luo, Nima Mesgarani
Submitted On:
19 April 2018 - 2:11pm
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ICASSP2018-poster.pdf

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[1] Yi Luo, Nima Mesgarani, "TasNet: time-domain audio separation network for real-time, single-channel speech separation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2987. Accessed: Aug. 21, 2018.
@article{2987-18,
url = {http://sigport.org/2987},
author = {Yi Luo; Nima Mesgarani },
publisher = {IEEE SigPort},
title = {TasNet: time-domain audio separation network for real-time, single-channel speech separation},
year = {2018} }
TY - EJOUR
T1 - TasNet: time-domain audio separation network for real-time, single-channel speech separation
AU - Yi Luo; Nima Mesgarani
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2987
ER -
Yi Luo, Nima Mesgarani. (2018). TasNet: time-domain audio separation network for real-time, single-channel speech separation. IEEE SigPort. http://sigport.org/2987
Yi Luo, Nima Mesgarani, 2018. TasNet: time-domain audio separation network for real-time, single-channel speech separation. Available at: http://sigport.org/2987.
Yi Luo, Nima Mesgarani. (2018). "TasNet: time-domain audio separation network for real-time, single-channel speech separation." Web.
1. Yi Luo, Nima Mesgarani. TasNet: time-domain audio separation network for real-time, single-channel speech separation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2987

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