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Signal and System Modeling, Representation and Estimation

Squared-Loss Mutual Information via High-Dimension Coherence Matrix Estimation


Squared-loss mutual information (SMI) is a surrogate of Shannon mutual information that is more advantageous for estimation. On the other hand, the coherence matrix of a pair of random vectors, a power-normalized version of the sample cross-covariance matrix, is a well-known second-order statistic found in the core of fundamental signal processing problems, such as canonical correlation analysis (CCA).

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Authors:
Ferran de Cabrera, Jaume Riba
Submitted On:
10 May 2019 - 8:02am
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Squared-Loss Mutual Information via High-Dimension Coherence Matrix Estimation

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[1] Ferran de Cabrera, Jaume Riba, "Squared-Loss Mutual Information via High-Dimension Coherence Matrix Estimation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4315. Accessed: Jul. 20, 2019.
@article{4315-19,
url = {http://sigport.org/4315},
author = {Ferran de Cabrera; Jaume Riba },
publisher = {IEEE SigPort},
title = {Squared-Loss Mutual Information via High-Dimension Coherence Matrix Estimation},
year = {2019} }
TY - EJOUR
T1 - Squared-Loss Mutual Information via High-Dimension Coherence Matrix Estimation
AU - Ferran de Cabrera; Jaume Riba
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4315
ER -
Ferran de Cabrera, Jaume Riba. (2019). Squared-Loss Mutual Information via High-Dimension Coherence Matrix Estimation. IEEE SigPort. http://sigport.org/4315
Ferran de Cabrera, Jaume Riba, 2019. Squared-Loss Mutual Information via High-Dimension Coherence Matrix Estimation. Available at: http://sigport.org/4315.
Ferran de Cabrera, Jaume Riba. (2019). "Squared-Loss Mutual Information via High-Dimension Coherence Matrix Estimation." Web.
1. Ferran de Cabrera, Jaume Riba. Squared-Loss Mutual Information via High-Dimension Coherence Matrix Estimation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4315

On the Fourier Representation of Computable Continuous Signals

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Authors:
Holger Boche, Ullrich J. Mönich
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9 May 2019 - 4:44am
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icassp2019_uniform.pdf

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[1] Holger Boche, Ullrich J. Mönich, "On the Fourier Representation of Computable Continuous Signals", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4170. Accessed: Jul. 20, 2019.
@article{4170-19,
url = {http://sigport.org/4170},
author = {Holger Boche; Ullrich J. Mönich },
publisher = {IEEE SigPort},
title = {On the Fourier Representation of Computable Continuous Signals},
year = {2019} }
TY - EJOUR
T1 - On the Fourier Representation of Computable Continuous Signals
AU - Holger Boche; Ullrich J. Mönich
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4170
ER -
Holger Boche, Ullrich J. Mönich. (2019). On the Fourier Representation of Computable Continuous Signals. IEEE SigPort. http://sigport.org/4170
Holger Boche, Ullrich J. Mönich, 2019. On the Fourier Representation of Computable Continuous Signals. Available at: http://sigport.org/4170.
Holger Boche, Ullrich J. Mönich. (2019). "On the Fourier Representation of Computable Continuous Signals." Web.
1. Holger Boche, Ullrich J. Mönich. On the Fourier Representation of Computable Continuous Signals [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4170

Toward Robust Interpretable Human Movement Pattern Analysis in a Workplace Setting


Gaining a better understanding of how people move about and interact with their environment is an important piece of understanding human behavior. Careful analysis of individuals’ deviations or variations in movement over time can provide an awareness about changes to their physical or mental state and may be helpful in tracking performance and well-being especially in workplace settings. We propose a technique for clustering and discovering patterns in human movement data by extracting motifs from the time series of durations where participants linger at different locations.

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Authors:
Brandon M. Booth, Tiantian Feng, Abhishek Jangalwa, Shrikanth S. Narayanan
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8 May 2019 - 4:45pm
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[1] Brandon M. Booth, Tiantian Feng, Abhishek Jangalwa, Shrikanth S. Narayanan, "Toward Robust Interpretable Human Movement Pattern Analysis in a Workplace Setting", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4134. Accessed: Jul. 20, 2019.
@article{4134-19,
url = {http://sigport.org/4134},
author = {Brandon M. Booth; Tiantian Feng; Abhishek Jangalwa; Shrikanth S. Narayanan },
publisher = {IEEE SigPort},
title = {Toward Robust Interpretable Human Movement Pattern Analysis in a Workplace Setting},
year = {2019} }
TY - EJOUR
T1 - Toward Robust Interpretable Human Movement Pattern Analysis in a Workplace Setting
AU - Brandon M. Booth; Tiantian Feng; Abhishek Jangalwa; Shrikanth S. Narayanan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4134
ER -
Brandon M. Booth, Tiantian Feng, Abhishek Jangalwa, Shrikanth S. Narayanan. (2019). Toward Robust Interpretable Human Movement Pattern Analysis in a Workplace Setting. IEEE SigPort. http://sigport.org/4134
Brandon M. Booth, Tiantian Feng, Abhishek Jangalwa, Shrikanth S. Narayanan, 2019. Toward Robust Interpretable Human Movement Pattern Analysis in a Workplace Setting. Available at: http://sigport.org/4134.
Brandon M. Booth, Tiantian Feng, Abhishek Jangalwa, Shrikanth S. Narayanan. (2019). "Toward Robust Interpretable Human Movement Pattern Analysis in a Workplace Setting." Web.
1. Brandon M. Booth, Tiantian Feng, Abhishek Jangalwa, Shrikanth S. Narayanan. Toward Robust Interpretable Human Movement Pattern Analysis in a Workplace Setting [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4134

Data-driven simulation using the nuclear norm heuristic


Applications of signal processing and control are classically model-based, involving a two-step procedure for modeling and design: first a model is built from given data, and second, the estimated model is used for filtering, estimation, or control. Both steps typically involve optimization problems, but the combination of both is not necessarily optimal, and the modeling step often ignores the ultimate design objective. Recently, data-driven alternatives are receiving attention, which employ a direct approach combining the modeling and design into a single step.

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Authors:
Ivan Markovsky
Submitted On:
8 May 2019 - 4:38am
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[1] Ivan Markovsky, "Data-driven simulation using the nuclear norm heuristic", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4048. Accessed: Jul. 20, 2019.
@article{4048-19,
url = {http://sigport.org/4048},
author = {Ivan Markovsky },
publisher = {IEEE SigPort},
title = {Data-driven simulation using the nuclear norm heuristic},
year = {2019} }
TY - EJOUR
T1 - Data-driven simulation using the nuclear norm heuristic
AU - Ivan Markovsky
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4048
ER -
Ivan Markovsky. (2019). Data-driven simulation using the nuclear norm heuristic. IEEE SigPort. http://sigport.org/4048
Ivan Markovsky, 2019. Data-driven simulation using the nuclear norm heuristic. Available at: http://sigport.org/4048.
Ivan Markovsky. (2019). "Data-driven simulation using the nuclear norm heuristic." Web.
1. Ivan Markovsky. Data-driven simulation using the nuclear norm heuristic [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4048

ALGEBRAICALLY-INITIALIZED EXPECTATION MAXIMIZATION FOR HEADER-FREE COMMUNICATION

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Authors:
Liangzu Peng, Xuming Song, Manolis C. Tsakiris, Hayoung Choi, Laurent Kneip, and Yuamming Shi
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7 May 2019 - 10:00pm
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ICASSP2019Poster

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[1] Liangzu Peng, Xuming Song, Manolis C. Tsakiris, Hayoung Choi, Laurent Kneip, and Yuamming Shi, "ALGEBRAICALLY-INITIALIZED EXPECTATION MAXIMIZATION FOR HEADER-FREE COMMUNICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3989. Accessed: Jul. 20, 2019.
@article{3989-19,
url = {http://sigport.org/3989},
author = {Liangzu Peng; Xuming Song; Manolis C. Tsakiris; Hayoung Choi; Laurent Kneip; and Yuamming Shi },
publisher = {IEEE SigPort},
title = {ALGEBRAICALLY-INITIALIZED EXPECTATION MAXIMIZATION FOR HEADER-FREE COMMUNICATION},
year = {2019} }
TY - EJOUR
T1 - ALGEBRAICALLY-INITIALIZED EXPECTATION MAXIMIZATION FOR HEADER-FREE COMMUNICATION
AU - Liangzu Peng; Xuming Song; Manolis C. Tsakiris; Hayoung Choi; Laurent Kneip; and Yuamming Shi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3989
ER -
Liangzu Peng, Xuming Song, Manolis C. Tsakiris, Hayoung Choi, Laurent Kneip, and Yuamming Shi. (2019). ALGEBRAICALLY-INITIALIZED EXPECTATION MAXIMIZATION FOR HEADER-FREE COMMUNICATION. IEEE SigPort. http://sigport.org/3989
Liangzu Peng, Xuming Song, Manolis C. Tsakiris, Hayoung Choi, Laurent Kneip, and Yuamming Shi, 2019. ALGEBRAICALLY-INITIALIZED EXPECTATION MAXIMIZATION FOR HEADER-FREE COMMUNICATION. Available at: http://sigport.org/3989.
Liangzu Peng, Xuming Song, Manolis C. Tsakiris, Hayoung Choi, Laurent Kneip, and Yuamming Shi. (2019). "ALGEBRAICALLY-INITIALIZED EXPECTATION MAXIMIZATION FOR HEADER-FREE COMMUNICATION." Web.
1. Liangzu Peng, Xuming Song, Manolis C. Tsakiris, Hayoung Choi, Laurent Kneip, and Yuamming Shi. ALGEBRAICALLY-INITIALIZED EXPECTATION MAXIMIZATION FOR HEADER-FREE COMMUNICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3989

Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters


The paper introduces a novel family of deterministic signals, the orthogonal periodic sequences (OPSs), for the identification of functional link polynomial (FLiP) filters. The novel sequences share many of the characteristics of the perfect periodic sequences (PPSs). As the PPSs, they allow the perfect identification of a FLiP filter on a finite time interval with the cross-correlation method. In contrast to the PPSs, OPSs can identify also non-orthogonal FLiP filters, as the Volterra filters.

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Authors:
Alberto Carini, Simone Orcioni, Stefania Cecchi
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7 May 2019 - 3:07pm
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[1] Alberto Carini, Simone Orcioni, Stefania Cecchi, "Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3948. Accessed: Jul. 20, 2019.
@article{3948-19,
url = {http://sigport.org/3948},
author = {Alberto Carini; Simone Orcioni; Stefania Cecchi },
publisher = {IEEE SigPort},
title = {Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters},
year = {2019} }
TY - EJOUR
T1 - Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters
AU - Alberto Carini; Simone Orcioni; Stefania Cecchi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3948
ER -
Alberto Carini, Simone Orcioni, Stefania Cecchi. (2019). Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters. IEEE SigPort. http://sigport.org/3948
Alberto Carini, Simone Orcioni, Stefania Cecchi, 2019. Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters. Available at: http://sigport.org/3948.
Alberto Carini, Simone Orcioni, Stefania Cecchi. (2019). "Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters." Web.
1. Alberto Carini, Simone Orcioni, Stefania Cecchi. Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3948

SPACE ALTERNATING VARIATIONAL ESTIMATION AND KRONECKER STRUCTURED DICTIONARY LEARNING


In this paper, we address the fundamental problem of Sparse
Bayesian Learning (SBL), where the received signal is a high-order
tensor. We furthermore consider the problem of dictionary learning
(DL), where the tensor observations are assumed to be generated
from a Kronecker structured (KS) dictionary matrix multiplied by
the sparse coefficients. Exploiting the tensorial structure results in
a reduction in the number of degrees of freedom in the learning
problem, since the dimensions of each of the factor matrices are significantly

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7 May 2019 - 12:58pm
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ICASSP19.pdf

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[1] , "SPACE ALTERNATING VARIATIONAL ESTIMATION AND KRONECKER STRUCTURED DICTIONARY LEARNING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3923. Accessed: Jul. 20, 2019.
@article{3923-19,
url = {http://sigport.org/3923},
author = { },
publisher = {IEEE SigPort},
title = {SPACE ALTERNATING VARIATIONAL ESTIMATION AND KRONECKER STRUCTURED DICTIONARY LEARNING},
year = {2019} }
TY - EJOUR
T1 - SPACE ALTERNATING VARIATIONAL ESTIMATION AND KRONECKER STRUCTURED DICTIONARY LEARNING
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3923
ER -
. (2019). SPACE ALTERNATING VARIATIONAL ESTIMATION AND KRONECKER STRUCTURED DICTIONARY LEARNING. IEEE SigPort. http://sigport.org/3923
, 2019. SPACE ALTERNATING VARIATIONAL ESTIMATION AND KRONECKER STRUCTURED DICTIONARY LEARNING. Available at: http://sigport.org/3923.
. (2019). "SPACE ALTERNATING VARIATIONAL ESTIMATION AND KRONECKER STRUCTURED DICTIONARY LEARNING." Web.
1. . SPACE ALTERNATING VARIATIONAL ESTIMATION AND KRONECKER STRUCTURED DICTIONARY LEARNING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3923

Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition


It is well known that empirical mode decomposition can suffer from computational instabilities at the signal boundaries. These ``end effects'' cause two problems: 1) sifting termination issues, i.e.~convergence and 2) estimation error, i.e.~accuracy. In this paper, we propose to use linear prediction in conjunction with a previous method to address end effects, to further mitigate these problems.

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Authors:
Steven Sandoval, Matthew Bredin, and Phillip L.~De Leon
Submitted On:
26 November 2018 - 4:23pm
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[1] Steven Sandoval, Matthew Bredin, and Phillip L.~De Leon, "Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3735. Accessed: Jul. 20, 2019.
@article{3735-18,
url = {http://sigport.org/3735},
author = {Steven Sandoval; Matthew Bredin; and Phillip L.~De Leon },
publisher = {IEEE SigPort},
title = {Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition},
year = {2018} }
TY - EJOUR
T1 - Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition
AU - Steven Sandoval; Matthew Bredin; and Phillip L.~De Leon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3735
ER -
Steven Sandoval, Matthew Bredin, and Phillip L.~De Leon. (2018). Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition. IEEE SigPort. http://sigport.org/3735
Steven Sandoval, Matthew Bredin, and Phillip L.~De Leon, 2018. Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition. Available at: http://sigport.org/3735.
Steven Sandoval, Matthew Bredin, and Phillip L.~De Leon. (2018). "Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition." Web.
1. Steven Sandoval, Matthew Bredin, and Phillip L.~De Leon. Using Linear Prediction to Mitigate End Effects in Empirical Mode Decomposition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3735

ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING


Without relative motion or beam scanning, orbitalangular-momentum (OAM)-based radar is shown to be able to estimate azimuth of targets, which opens a new perspective for traditional radar techniques. However, the existing application of two-dimensional (2-D) fast Fourier transform (FFT) and multiple signal classification (MUSIC) algorithms in OAM-based radar targets detection doesn’t realize 2-D super-resolution and robust estimation.

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Authors:
Rui Chen, Wen-xuan Long, Yue Gao and Jiandong Li
Submitted On:
22 November 2018 - 10:45pm
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ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL.pdf

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[1] Rui Chen, Wen-xuan Long, Yue Gao and Jiandong Li, "ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3729. Accessed: Jul. 20, 2019.
@article{3729-18,
url = {http://sigport.org/3729},
author = {Rui Chen; Wen-xuan Long; Yue Gao and Jiandong Li },
publisher = {IEEE SigPort},
title = {ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING},
year = {2018} }
TY - EJOUR
T1 - ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING
AU - Rui Chen; Wen-xuan Long; Yue Gao and Jiandong Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3729
ER -
Rui Chen, Wen-xuan Long, Yue Gao and Jiandong Li. (2018). ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING. IEEE SigPort. http://sigport.org/3729
Rui Chen, Wen-xuan Long, Yue Gao and Jiandong Li, 2018. ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING. Available at: http://sigport.org/3729.
Rui Chen, Wen-xuan Long, Yue Gao and Jiandong Li. (2018). "ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING." Web.
1. Rui Chen, Wen-xuan Long, Yue Gao and Jiandong Li. ORBITAL ANGULAR MOMENTUM-BASED TWO-DIMENSIONAL SUPER-RESOLUTION TARGETS IMAGING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3729

Learning Flexible Representations of Stochastic Processes on Graphs


Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations. However, these graph-dependent linear operations are developed for scalar functions supported on undirected graphs. We propose both a generalization of the underlying graph and a class of linear operations for stochastic (time-varying) processes on directed (or undirected) graphs to be used in graph convolutional networks.

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Authors:
Brian M. Sadler, Radu V. Balan
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30 May 2018 - 1:35pm
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[1] Brian M. Sadler, Radu V. Balan, "Learning Flexible Representations of Stochastic Processes on Graphs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3220. Accessed: Jul. 20, 2019.
@article{3220-18,
url = {http://sigport.org/3220},
author = {Brian M. Sadler; Radu V. Balan },
publisher = {IEEE SigPort},
title = {Learning Flexible Representations of Stochastic Processes on Graphs},
year = {2018} }
TY - EJOUR
T1 - Learning Flexible Representations of Stochastic Processes on Graphs
AU - Brian M. Sadler; Radu V. Balan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3220
ER -
Brian M. Sadler, Radu V. Balan. (2018). Learning Flexible Representations of Stochastic Processes on Graphs. IEEE SigPort. http://sigport.org/3220
Brian M. Sadler, Radu V. Balan, 2018. Learning Flexible Representations of Stochastic Processes on Graphs. Available at: http://sigport.org/3220.
Brian M. Sadler, Radu V. Balan. (2018). "Learning Flexible Representations of Stochastic Processes on Graphs." Web.
1. Brian M. Sadler, Radu V. Balan. Learning Flexible Representations of Stochastic Processes on Graphs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3220

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