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Emerging: Big Data

LEARNING GEOGRAPHICALLY DISTRIBUTED DATA FOR MULTIPLE TASKS USING GENERATIVE ADVERSARIAL NETWORKS


We present a novel method that supports the learning of multiple classification tasks from geographically distributed data. By combining locally trained generative adversarial networks (GANs) with a small fraction of original data samples, our proposed scheme can train multiple discriminative models at a central location with low communication overhead. Experiments using common image datasets (MNIST, CIFAR-10, LSUN-20, Celeb-A) show that our proposed scheme can achieve comparable classification accuracy as the ideal classifier trained using all data from all sites.

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Authors:
Yaqi Wang, Mehdi Nikkhah, Xiaoqing Zhu, Wai-tian Tan, and Rob Liston
Submitted On:
16 September 2019 - 11:19am
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[1] Yaqi Wang, Mehdi Nikkhah, Xiaoqing Zhu, Wai-tian Tan, and Rob Liston, "LEARNING GEOGRAPHICALLY DISTRIBUTED DATA FOR MULTIPLE TASKS USING GENERATIVE ADVERSARIAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4642. Accessed: Sep. 20, 2019.
@article{4642-19,
url = {http://sigport.org/4642},
author = {Yaqi Wang; Mehdi Nikkhah; Xiaoqing Zhu; Wai-tian Tan; and Rob Liston },
publisher = {IEEE SigPort},
title = {LEARNING GEOGRAPHICALLY DISTRIBUTED DATA FOR MULTIPLE TASKS USING GENERATIVE ADVERSARIAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - LEARNING GEOGRAPHICALLY DISTRIBUTED DATA FOR MULTIPLE TASKS USING GENERATIVE ADVERSARIAL NETWORKS
AU - Yaqi Wang; Mehdi Nikkhah; Xiaoqing Zhu; Wai-tian Tan; and Rob Liston
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4642
ER -
Yaqi Wang, Mehdi Nikkhah, Xiaoqing Zhu, Wai-tian Tan, and Rob Liston. (2019). LEARNING GEOGRAPHICALLY DISTRIBUTED DATA FOR MULTIPLE TASKS USING GENERATIVE ADVERSARIAL NETWORKS. IEEE SigPort. http://sigport.org/4642
Yaqi Wang, Mehdi Nikkhah, Xiaoqing Zhu, Wai-tian Tan, and Rob Liston, 2019. LEARNING GEOGRAPHICALLY DISTRIBUTED DATA FOR MULTIPLE TASKS USING GENERATIVE ADVERSARIAL NETWORKS. Available at: http://sigport.org/4642.
Yaqi Wang, Mehdi Nikkhah, Xiaoqing Zhu, Wai-tian Tan, and Rob Liston. (2019). "LEARNING GEOGRAPHICALLY DISTRIBUTED DATA FOR MULTIPLE TASKS USING GENERATIVE ADVERSARIAL NETWORKS." Web.
1. Yaqi Wang, Mehdi Nikkhah, Xiaoqing Zhu, Wai-tian Tan, and Rob Liston. LEARNING GEOGRAPHICALLY DISTRIBUTED DATA FOR MULTIPLE TASKS USING GENERATIVE ADVERSARIAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4642

A Stochastic LBFGS Algorithm for Radio Interferometric Calibration


We present a stochastic, limited-memory Broyden Fletcher Goldfarb Shanno (LBFGS) algorithm that is suitable for handling very large amounts of data. A direct application of this algorithm is radio interferometric calibration of raw data at fine time and frequency resolution. Almost all existing radio interferometric calibration algorithms assume that it is possible to fit the dataset being calibrated into memory. Therefore, the raw data is averaged in time and frequency to reduce its size by many orders of magnitude before calibration is performed.

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Authors:
Sarod Yatawatta, Lukas De Clercq, Hanno Spreeuw, Faruk Diblen
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4 June 2019 - 5:43pm
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[1] Sarod Yatawatta, Lukas De Clercq, Hanno Spreeuw, Faruk Diblen, "A Stochastic LBFGS Algorithm for Radio Interferometric Calibration", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4564. Accessed: Sep. 20, 2019.
@article{4564-19,
url = {http://sigport.org/4564},
author = {Sarod Yatawatta; Lukas De Clercq; Hanno Spreeuw; Faruk Diblen },
publisher = {IEEE SigPort},
title = {A Stochastic LBFGS Algorithm for Radio Interferometric Calibration},
year = {2019} }
TY - EJOUR
T1 - A Stochastic LBFGS Algorithm for Radio Interferometric Calibration
AU - Sarod Yatawatta; Lukas De Clercq; Hanno Spreeuw; Faruk Diblen
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4564
ER -
Sarod Yatawatta, Lukas De Clercq, Hanno Spreeuw, Faruk Diblen. (2019). A Stochastic LBFGS Algorithm for Radio Interferometric Calibration. IEEE SigPort. http://sigport.org/4564
Sarod Yatawatta, Lukas De Clercq, Hanno Spreeuw, Faruk Diblen, 2019. A Stochastic LBFGS Algorithm for Radio Interferometric Calibration. Available at: http://sigport.org/4564.
Sarod Yatawatta, Lukas De Clercq, Hanno Spreeuw, Faruk Diblen. (2019). "A Stochastic LBFGS Algorithm for Radio Interferometric Calibration." Web.
1. Sarod Yatawatta, Lukas De Clercq, Hanno Spreeuw, Faruk Diblen. A Stochastic LBFGS Algorithm for Radio Interferometric Calibration [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4564

A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties


Stochastic mirror descent (SMD) algorithms have recently garnered a great deal of attention in optimization, signal processing, and machine learning. They are similar to stochastic gradient descent (SGD), in that they perform updates along the negative gradient of an instantaneous (or stochastically chosen) loss function. However, rather than update the parameter (or weight) vector directly, they update it in a "mirrored" domain whose transformation is given by the gradient of a strictly convex differentiable potential function.

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Authors:
Navid Azizan, Babak Hassibi
Submitted On:
13 May 2019 - 8:33pm
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[1] Navid Azizan, Babak Hassibi, "A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4498. Accessed: Sep. 20, 2019.
@article{4498-19,
url = {http://sigport.org/4498},
author = {Navid Azizan; Babak Hassibi },
publisher = {IEEE SigPort},
title = {A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties},
year = {2019} }
TY - EJOUR
T1 - A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties
AU - Navid Azizan; Babak Hassibi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4498
ER -
Navid Azizan, Babak Hassibi. (2019). A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties. IEEE SigPort. http://sigport.org/4498
Navid Azizan, Babak Hassibi, 2019. A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties. Available at: http://sigport.org/4498.
Navid Azizan, Babak Hassibi. (2019). "A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties." Web.
1. Navid Azizan, Babak Hassibi. A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4498

MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE


Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations.

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Authors:
Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis
Submitted On:
11 May 2019 - 1:38am
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[1] Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis, "MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4435. Accessed: Sep. 20, 2019.
@article{4435-19,
url = {http://sigport.org/4435},
author = {Evaggelia Tsiligianni; Angel Lopez Aguirre; Valerio Panzica La Manna; Frank Pasveer; Wilfried Philips; Nikos Deligiannis },
publisher = {IEEE SigPort},
title = {MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE},
year = {2019} }
TY - EJOUR
T1 - MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE
AU - Evaggelia Tsiligianni; Angel Lopez Aguirre; Valerio Panzica La Manna; Frank Pasveer; Wilfried Philips; Nikos Deligiannis
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4435
ER -
Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis. (2019). MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE. IEEE SigPort. http://sigport.org/4435
Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis, 2019. MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE. Available at: http://sigport.org/4435.
Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis. (2019). "MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE." Web.
1. Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis. MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4435

Kernel Random Matrices of Large Concentrated Data: The Example of GAN-Generated Images


Based on recent random matrix advances in the analysis of kernel methods for classification and clustering, this paper proposes the study of large kernel methods for a wide class of random inputs, i.e., concentrated data, which are more generic than Gaussian mixtures. The concentration assumption is motivated by the fact that one can use generative models to design complex data structures, through Lipschitz-ally transformed concentrated vectors (e.g., Gaussian) which remain concentrated vectors.

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Authors:
Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet
Submitted On:
10 May 2019 - 4:02pm
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[1] Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet, "Kernel Random Matrices of Large Concentrated Data: The Example of GAN-Generated Images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4398. Accessed: Sep. 20, 2019.
@article{4398-19,
url = {http://sigport.org/4398},
author = {Mohamed El Amine Seddik; Mohamed Tamaazousti; Romain Couillet },
publisher = {IEEE SigPort},
title = {Kernel Random Matrices of Large Concentrated Data: The Example of GAN-Generated Images},
year = {2019} }
TY - EJOUR
T1 - Kernel Random Matrices of Large Concentrated Data: The Example of GAN-Generated Images
AU - Mohamed El Amine Seddik; Mohamed Tamaazousti; Romain Couillet
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4398
ER -
Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet. (2019). Kernel Random Matrices of Large Concentrated Data: The Example of GAN-Generated Images. IEEE SigPort. http://sigport.org/4398
Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet, 2019. Kernel Random Matrices of Large Concentrated Data: The Example of GAN-Generated Images. Available at: http://sigport.org/4398.
Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet. (2019). "Kernel Random Matrices of Large Concentrated Data: The Example of GAN-Generated Images." Web.
1. Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet. Kernel Random Matrices of Large Concentrated Data: The Example of GAN-Generated Images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4398

CrowNN: Human-in-the-loop Network with Crowd-generated Inputs


Input features are indispensable for almost all machine learning methods; however, their definitions themselves are sometimes too abstract to extract automatically. Human-in-the- loop machine learning is a promising solution to such cases where humans extract the feature values for machine learning models. We use crowdsourcing for feature value extraction and consider a problem to aggregate the feature values to improve machine learning classifiers.

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Authors:
Yukino Baba, Hisashi Kashima
Submitted On:
10 May 2019 - 4:36am
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[1] Yukino Baba, Hisashi Kashima, "CrowNN: Human-in-the-loop Network with Crowd-generated Inputs", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4289. Accessed: Sep. 20, 2019.
@article{4289-19,
url = {http://sigport.org/4289},
author = {Yukino Baba; Hisashi Kashima },
publisher = {IEEE SigPort},
title = {CrowNN: Human-in-the-loop Network with Crowd-generated Inputs},
year = {2019} }
TY - EJOUR
T1 - CrowNN: Human-in-the-loop Network with Crowd-generated Inputs
AU - Yukino Baba; Hisashi Kashima
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4289
ER -
Yukino Baba, Hisashi Kashima. (2019). CrowNN: Human-in-the-loop Network with Crowd-generated Inputs. IEEE SigPort. http://sigport.org/4289
Yukino Baba, Hisashi Kashima, 2019. CrowNN: Human-in-the-loop Network with Crowd-generated Inputs. Available at: http://sigport.org/4289.
Yukino Baba, Hisashi Kashima. (2019). "CrowNN: Human-in-the-loop Network with Crowd-generated Inputs." Web.
1. Yukino Baba, Hisashi Kashima. CrowNN: Human-in-the-loop Network with Crowd-generated Inputs [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4289

Fuzzy Personalized Scoring Model for Recommendation System


In this research, we aim to propose a data preprocessing framework particularly for financial sector to generate the rating data as input to the collaborative system. First, clustering technique is applied to cluster all users based on their demographic information which might be able to differentiate the customers’ background. Then, for each customer group, the importance of demographic characteristics which are highly associated with financial products purchasing are analyzed by the proposed fuzzy integral technique.

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Authors:
Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng
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8 May 2019 - 7:40am
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[1] Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng, "Fuzzy Personalized Scoring Model for Recommendation System", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4077. Accessed: Sep. 20, 2019.
@article{4077-19,
url = {http://sigport.org/4077},
author = {Chao-Lung Yang; Shang-Che Hsu; Kai-Lung Hua; Wen-Huang Cheng },
publisher = {IEEE SigPort},
title = {Fuzzy Personalized Scoring Model for Recommendation System},
year = {2019} }
TY - EJOUR
T1 - Fuzzy Personalized Scoring Model for Recommendation System
AU - Chao-Lung Yang; Shang-Che Hsu; Kai-Lung Hua; Wen-Huang Cheng
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4077
ER -
Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng. (2019). Fuzzy Personalized Scoring Model for Recommendation System. IEEE SigPort. http://sigport.org/4077
Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng, 2019. Fuzzy Personalized Scoring Model for Recommendation System. Available at: http://sigport.org/4077.
Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng. (2019). "Fuzzy Personalized Scoring Model for Recommendation System." Web.
1. Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng. Fuzzy Personalized Scoring Model for Recommendation System [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4077

CALVI_ICASSP

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Authors:
Giuseppe G. Calvi, Vladimir Lucic, Danilo P. Mandic
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7 May 2019 - 5:22pm
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[1] Giuseppe G. Calvi, Vladimir Lucic, Danilo P. Mandic, "CALVI_ICASSP", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3959. Accessed: Sep. 20, 2019.
@article{3959-19,
url = {http://sigport.org/3959},
author = {Giuseppe G. Calvi; Vladimir Lucic; Danilo P. Mandic },
publisher = {IEEE SigPort},
title = {CALVI_ICASSP},
year = {2019} }
TY - EJOUR
T1 - CALVI_ICASSP
AU - Giuseppe G. Calvi; Vladimir Lucic; Danilo P. Mandic
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3959
ER -
Giuseppe G. Calvi, Vladimir Lucic, Danilo P. Mandic. (2019). CALVI_ICASSP. IEEE SigPort. http://sigport.org/3959
Giuseppe G. Calvi, Vladimir Lucic, Danilo P. Mandic, 2019. CALVI_ICASSP. Available at: http://sigport.org/3959.
Giuseppe G. Calvi, Vladimir Lucic, Danilo P. Mandic. (2019). "CALVI_ICASSP." Web.
1. Giuseppe G. Calvi, Vladimir Lucic, Danilo P. Mandic. CALVI_ICASSP [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3959

BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS

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Authors:
Chi-Han Du, Ming-Feng Tsai, Chuan-Ju Wang
Submitted On:
27 March 2019 - 9:06am
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[1] Chi-Han Du, Ming-Feng Tsai, Chuan-Ju Wang, "BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3857. Accessed: Sep. 20, 2019.
@article{3857-19,
url = {http://sigport.org/3857},
author = {Chi-Han Du; Ming-Feng Tsai; Chuan-Ju Wang },
publisher = {IEEE SigPort},
title = {BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS},
year = {2019} }
TY - EJOUR
T1 - BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS
AU - Chi-Han Du; Ming-Feng Tsai; Chuan-Ju Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3857
ER -
Chi-Han Du, Ming-Feng Tsai, Chuan-Ju Wang. (2019). BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS. IEEE SigPort. http://sigport.org/3857
Chi-Han Du, Ming-Feng Tsai, Chuan-Ju Wang, 2019. BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS. Available at: http://sigport.org/3857.
Chi-Han Du, Ming-Feng Tsai, Chuan-Ju Wang. (2019). "BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS." Web.
1. Chi-Han Du, Ming-Feng Tsai, Chuan-Ju Wang. BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3857

GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)


We reveal an interesting link between tensors and multivariate statistics. The rank of a multivariate probability tensor can be interpreted as a nonlinear measure of statistical dependence of the associated random variables. Rank equals one when the random variables are independent, and complete statistical dependence corresponds to full rank; but we show that rank as low as two can already model strong statistical dependence.

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Authors:
N.D. Sidiropoulos, N. Kargas, X. Fu
Submitted On:
24 December 2018 - 8:25pm
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GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)

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[1] N.D. Sidiropoulos, N. Kargas, X. Fu, "GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3842. Accessed: Sep. 20, 2019.
@article{3842-18,
url = {http://sigport.org/3842},
author = {N.D. Sidiropoulos; N. Kargas; X. Fu },
publisher = {IEEE SigPort},
title = {GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)},
year = {2018} }
TY - EJOUR
T1 - GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)
AU - N.D. Sidiropoulos; N. Kargas; X. Fu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3842
ER -
N.D. Sidiropoulos, N. Kargas, X. Fu. (2018). GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu). IEEE SigPort. http://sigport.org/3842
N.D. Sidiropoulos, N. Kargas, X. Fu, 2018. GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu). Available at: http://sigport.org/3842.
N.D. Sidiropoulos, N. Kargas, X. Fu. (2018). "GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)." Web.
1. N.D. Sidiropoulos, N. Kargas, X. Fu. GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu) [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3842

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