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Bayesian learning; Bayesian signal processing (MLR-BAYL)

Variational Inference for Nonparametric Subspace Dictionary Learning with Hierarchical Beta Process

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Authors:
Shaoyang Li, Xiaoming Tao, Jianhua Lu
Submitted On:
28 February 2017 - 7:43am
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Poster-Shaoyang Li-ICASSP17-2987.pdf

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[1] Shaoyang Li, Xiaoming Tao, Jianhua Lu, "Variational Inference for Nonparametric Subspace Dictionary Learning with Hierarchical Beta Process", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1507. Accessed: May. 28, 2017.
@article{1507-17,
url = {http://sigport.org/1507},
author = {Shaoyang Li; Xiaoming Tao; Jianhua Lu },
publisher = {IEEE SigPort},
title = {Variational Inference for Nonparametric Subspace Dictionary Learning with Hierarchical Beta Process},
year = {2017} }
TY - EJOUR
T1 - Variational Inference for Nonparametric Subspace Dictionary Learning with Hierarchical Beta Process
AU - Shaoyang Li; Xiaoming Tao; Jianhua Lu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1507
ER -
Shaoyang Li, Xiaoming Tao, Jianhua Lu. (2017). Variational Inference for Nonparametric Subspace Dictionary Learning with Hierarchical Beta Process. IEEE SigPort. http://sigport.org/1507
Shaoyang Li, Xiaoming Tao, Jianhua Lu, 2017. Variational Inference for Nonparametric Subspace Dictionary Learning with Hierarchical Beta Process. Available at: http://sigport.org/1507.
Shaoyang Li, Xiaoming Tao, Jianhua Lu. (2017). "Variational Inference for Nonparametric Subspace Dictionary Learning with Hierarchical Beta Process." Web.
1. Shaoyang Li, Xiaoming Tao, Jianhua Lu. Variational Inference for Nonparametric Subspace Dictionary Learning with Hierarchical Beta Process [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1507

DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA


Non-parametric Bayesian methods have recently gained popularity in several research areas dealing with unsupervised learning. These models are capable of simultaneously learning the cluster models as well as their number based on properties of a dataset. The most commonly applied models are using Dirichlet process priors and Gaussian models, called as Dirichlet process Gaussian mixture models (DPGMMs). Recently, von Mises-Fisher mixture models (VMMs) have also been gaining popularity in modelling high-dimensional unit-normalized features such as text documents and gene expression data.

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Authors:
Ulpu Remes, Okko Räsänen
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28 February 2017 - 7:05am
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i-vector clustering with DPMMs

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[1] Ulpu Remes, Okko Räsänen, "DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1504. Accessed: May. 28, 2017.
@article{1504-17,
url = {http://sigport.org/1504},
author = {Ulpu Remes; Okko Räsänen },
publisher = {IEEE SigPort},
title = {DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA},
year = {2017} }
TY - EJOUR
T1 - DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA
AU - Ulpu Remes; Okko Räsänen
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1504
ER -
Ulpu Remes, Okko Räsänen. (2017). DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA. IEEE SigPort. http://sigport.org/1504
Ulpu Remes, Okko Räsänen, 2017. DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA. Available at: http://sigport.org/1504.
Ulpu Remes, Okko Räsänen. (2017). "DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA." Web.
1. Ulpu Remes, Okko Räsänen. DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1504

Learning Structural Properties of Wireless Ad-Hoc Networks Non-Parametrically from Spectral Activity Samples

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Authors:
Silvija Kokalj-Filipovic, Crystal Acosta, Michael Pepe
Submitted On:
6 December 2016 - 12:30pm
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GlobalSIPNS3HSMMtalk.pdf

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[1] Silvija Kokalj-Filipovic, Crystal Acosta, Michael Pepe, "Learning Structural Properties of Wireless Ad-Hoc Networks Non-Parametrically from Spectral Activity Samples", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1369. Accessed: May. 28, 2017.
@article{1369-16,
url = {http://sigport.org/1369},
author = {Silvija Kokalj-Filipovic; Crystal Acosta; Michael Pepe },
publisher = {IEEE SigPort},
title = {Learning Structural Properties of Wireless Ad-Hoc Networks Non-Parametrically from Spectral Activity Samples},
year = {2016} }
TY - EJOUR
T1 - Learning Structural Properties of Wireless Ad-Hoc Networks Non-Parametrically from Spectral Activity Samples
AU - Silvija Kokalj-Filipovic; Crystal Acosta; Michael Pepe
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1369
ER -
Silvija Kokalj-Filipovic, Crystal Acosta, Michael Pepe. (2016). Learning Structural Properties of Wireless Ad-Hoc Networks Non-Parametrically from Spectral Activity Samples. IEEE SigPort. http://sigport.org/1369
Silvija Kokalj-Filipovic, Crystal Acosta, Michael Pepe, 2016. Learning Structural Properties of Wireless Ad-Hoc Networks Non-Parametrically from Spectral Activity Samples. Available at: http://sigport.org/1369.
Silvija Kokalj-Filipovic, Crystal Acosta, Michael Pepe. (2016). "Learning Structural Properties of Wireless Ad-Hoc Networks Non-Parametrically from Spectral Activity Samples." Web.
1. Silvija Kokalj-Filipovic, Crystal Acosta, Michael Pepe. Learning Structural Properties of Wireless Ad-Hoc Networks Non-Parametrically from Spectral Activity Samples [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1369

Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling


We consider the problem of estimating discrete self- exciting point process models from limited binary observations, where the history of the process serves as the covariate. We analyze the performance of two classes of estimators: l1-regularized maximum likelihood and greedy estimation for a discrete version of the Hawkes process and characterize the sampling tradeoffs required for stable recovery in the non-asymptotic regime. Our results extend those of compressed sensing for linear and generalized linear models with i.i.d.

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Authors:
Abbas Kazemipour, Min Wu and Behtash Babadi
Submitted On:
12 December 2016 - 9:35am
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Robust_SEPP_TSP.pdf

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[1] Abbas Kazemipour, Min Wu and Behtash Babadi, "Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1261. Accessed: May. 28, 2017.
@article{1261-16,
url = {http://sigport.org/1261},
author = {Abbas Kazemipour; Min Wu and Behtash Babadi },
publisher = {IEEE SigPort},
title = {Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling},
year = {2016} }
TY - EJOUR
T1 - Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling
AU - Abbas Kazemipour; Min Wu and Behtash Babadi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1261
ER -
Abbas Kazemipour, Min Wu and Behtash Babadi. (2016). Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling. IEEE SigPort. http://sigport.org/1261
Abbas Kazemipour, Min Wu and Behtash Babadi, 2016. Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling. Available at: http://sigport.org/1261.
Abbas Kazemipour, Min Wu and Behtash Babadi. (2016). "Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling." Web.
1. Abbas Kazemipour, Min Wu and Behtash Babadi. Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1261

A Nonparametric Bayesian Approach to Joint Multiple Dictionary Learning with Separate Image Sources

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23 February 2016 - 1:44pm
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GlobalSip2015 - Shaoyang Li - Dec16.pdf

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[1] , "A Nonparametric Bayesian Approach to Joint Multiple Dictionary Learning with Separate Image Sources", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/485. Accessed: May. 28, 2017.
@article{485-15,
url = {http://sigport.org/485},
author = { },
publisher = {IEEE SigPort},
title = {A Nonparametric Bayesian Approach to Joint Multiple Dictionary Learning with Separate Image Sources},
year = {2015} }
TY - EJOUR
T1 - A Nonparametric Bayesian Approach to Joint Multiple Dictionary Learning with Separate Image Sources
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/485
ER -
. (2015). A Nonparametric Bayesian Approach to Joint Multiple Dictionary Learning with Separate Image Sources. IEEE SigPort. http://sigport.org/485
, 2015. A Nonparametric Bayesian Approach to Joint Multiple Dictionary Learning with Separate Image Sources. Available at: http://sigport.org/485.
. (2015). "A Nonparametric Bayesian Approach to Joint Multiple Dictionary Learning with Separate Image Sources." Web.
1. . A Nonparametric Bayesian Approach to Joint Multiple Dictionary Learning with Separate Image Sources [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/485

Inference of Sparse Gene Regulatory Network from RNA-Seq Time Series Data

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Authors:
Tao Hu
Submitted On:
23 February 2016 - 1:44pm
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GlobalSIP2015.pdf

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[1] Tao Hu, "Inference of Sparse Gene Regulatory Network from RNA-Seq Time Series Data", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/446. Accessed: May. 28, 2017.
@article{446-15,
url = {http://sigport.org/446},
author = {Tao Hu },
publisher = {IEEE SigPort},
title = {Inference of Sparse Gene Regulatory Network from RNA-Seq Time Series Data},
year = {2015} }
TY - EJOUR
T1 - Inference of Sparse Gene Regulatory Network from RNA-Seq Time Series Data
AU - Tao Hu
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/446
ER -
Tao Hu. (2015). Inference of Sparse Gene Regulatory Network from RNA-Seq Time Series Data. IEEE SigPort. http://sigport.org/446
Tao Hu, 2015. Inference of Sparse Gene Regulatory Network from RNA-Seq Time Series Data. Available at: http://sigport.org/446.
Tao Hu. (2015). "Inference of Sparse Gene Regulatory Network from RNA-Seq Time Series Data." Web.
1. Tao Hu. Inference of Sparse Gene Regulatory Network from RNA-Seq Time Series Data [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/446