Sorry, you need to enable JavaScript to visit this website.

Bayesian learning; Bayesian signal processing (MLR-BAYL)

Sparse Bayesian Learning for Robust PCA

Jing Liu
YACONG DING

Paper Details

Authors:
Jing Liu
YACONG DING
Bhaskar Rao
Submitted On:
10 May 2019 - 5:46pm
Short Link:
Type:
Event:
Paper Code:
Document Year:
Cite

Document Files

SBL_RPCA.pdf

(19)

Keywords

Subscribe

Send Author a Private Message
[1] Bhaskar Rao, "Sparse Bayesian Learning for Robust PCA", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4415. Accessed: Jul. 19, 2019.
@article{4415-19,
url = {http://sigport.org/4415},
author = {Bhaskar Rao },
publisher = {IEEE SigPort},
title = {Sparse Bayesian Learning for Robust PCA},
year = {2019} }
TY - EJOUR
T1 - Sparse Bayesian Learning for Robust PCA
AU - Bhaskar Rao
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4415
ER -
Bhaskar Rao. (2019). Sparse Bayesian Learning for Robust PCA. IEEE SigPort. http://sigport.org/4415
Bhaskar Rao, 2019. Sparse Bayesian Learning for Robust PCA. Available at: http://sigport.org/4415.
Bhaskar Rao. (2019). "Sparse Bayesian Learning for Robust PCA." Web.
1. Bhaskar Rao. Sparse Bayesian Learning for Robust PCA [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4415

Adversarial variational Bayes methods for Tweedie compound Poisson mixed models

Yuanyuan Liu

The Tweedie Compound Poisson-Gamma model is routinely used for modeling non-negative continuous data with a discrete probability mass at zero. Mixed models with random effects account for the covariance structure related to the grouping hierarchy in the data. An important application of Tweedie mixed models is pricing the insurance policies, e.g. car insurance. However, the intractable likelihood function, the unknown variance function, and the hierarchical structure of mixed effects have presented considerable challenges for drawing inferences on Tweedie.

Paper Details

Authors:
Yuanyuan Liu
Submitted On:
10 May 2019 - 1:24pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster.pdf

(28)

Keywords

Subscribe

Send Author a Private Message
[1] , "Adversarial variational Bayes methods for Tweedie compound Poisson mixed models", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4375. Accessed: Jul. 19, 2019.
@article{4375-19,
url = {http://sigport.org/4375},
author = { },
publisher = {IEEE SigPort},
title = {Adversarial variational Bayes methods for Tweedie compound Poisson mixed models},
year = {2019} }
TY - EJOUR
T1 - Adversarial variational Bayes methods for Tweedie compound Poisson mixed models
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4375
ER -
. (2019). Adversarial variational Bayes methods for Tweedie compound Poisson mixed models. IEEE SigPort. http://sigport.org/4375
, 2019. Adversarial variational Bayes methods for Tweedie compound Poisson mixed models. Available at: http://sigport.org/4375.
. (2019). "Adversarial variational Bayes methods for Tweedie compound Poisson mixed models." Web.
1. . Adversarial variational Bayes methods for Tweedie compound Poisson mixed models [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4375

UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE

Yanna Cavalcanti

Paper Details

Authors:
Yanna Cavalcanti
Yanna Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Clovis Tauber
Submitted On:
10 May 2019 - 10:54am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster_icassp.pdf

(17)

Keywords

Subscribe

Send Author a Private Message
[1] Yanna Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Clovis Tauber, "UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4350. Accessed: Jul. 19, 2019.
@article{4350-19,
url = {http://sigport.org/4350},
author = {Yanna Cavalcanti; Thomas Oberlin; Nicolas Dobigeon; Cédric Févotte; Simon Stute; Clovis Tauber },
publisher = {IEEE SigPort},
title = {UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE},
year = {2019} }
TY - EJOUR
T1 - UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE
AU - Yanna Cavalcanti; Thomas Oberlin; Nicolas Dobigeon; Cédric Févotte; Simon Stute; Clovis Tauber
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4350
ER -
Yanna Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Clovis Tauber. (2019). UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE. IEEE SigPort. http://sigport.org/4350
Yanna Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Clovis Tauber, 2019. UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE. Available at: http://sigport.org/4350.
Yanna Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Clovis Tauber. (2019). "UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE." Web.
1. Yanna Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Clovis Tauber. UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4350

Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method

Guillem Boquet Pujadas

Road traffic forecasting systems are in scenarios where sensor or system failure occur. In those scenarios, it is known that missing values negatively affect estimation accuracy although it is being often underestimate in current deep neural network approaches. Our assumption is that traffic data can be generated from a latent space. Thus, we propose an online unsupervised data imputation method based on learning the data distribution using a variational autoencoder (VAE).

Paper Details

Authors:
Guillem Boquet Pujadas
Guillem Boquet, Jose Lopez Vicario, Antoni Morell, Javier Serrano
Submitted On:
10 May 2019 - 7:11am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Slides_WIN_v2.pdf

(18)

Keywords

Subscribe

Send Author a Private Message
[1] Guillem Boquet, Jose Lopez Vicario, Antoni Morell, Javier Serrano, "Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method ", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4303. Accessed: Jul. 19, 2019.
@article{4303-19,
url = {http://sigport.org/4303},
author = {Guillem Boquet; Jose Lopez Vicario; Antoni Morell; Javier Serrano },
publisher = {IEEE SigPort},
title = {Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method },
year = {2019} }
TY - EJOUR
T1 - Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method
AU - Guillem Boquet; Jose Lopez Vicario; Antoni Morell; Javier Serrano
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4303
ER -
Guillem Boquet, Jose Lopez Vicario, Antoni Morell, Javier Serrano. (2019). Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method . IEEE SigPort. http://sigport.org/4303
Guillem Boquet, Jose Lopez Vicario, Antoni Morell, Javier Serrano, 2019. Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method . Available at: http://sigport.org/4303.
Guillem Boquet, Jose Lopez Vicario, Antoni Morell, Javier Serrano. (2019). "Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method ." Web.
1. Guillem Boquet, Jose Lopez Vicario, Antoni Morell, Javier Serrano. Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4303

ENLLVM: Ensemble based Nonlinear Bayesian Filtering using Linear Latent Variable Models

Xiao Lin
Gabriel Terejanu

Real-time nonlinear Bayesian filtering algorithms are overwhelmed by data volume, velocity and increasing complexity of computational models. In this paper, we propose a novel ensemble based nonlinear Bayesian filtering approach which only requires a small number of simulations and can be applied to high-dimensional systems in the presence of intractable likelihood functions.

Paper Details

Authors:
Xiao Lin
Gabriel Terejanu
Submitted On:
8 May 2019 - 2:26pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ENLLVM: Ensemble based Nonlinear Bayesian Filtering using Linear Latent Variable Models

(14)

Keywords

Subscribe

Send Author a Private Message
[1] , "ENLLVM: Ensemble based Nonlinear Bayesian Filtering using Linear Latent Variable Models", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4128. Accessed: Jul. 19, 2019.
@article{4128-19,
url = {http://sigport.org/4128},
author = { },
publisher = {IEEE SigPort},
title = {ENLLVM: Ensemble based Nonlinear Bayesian Filtering using Linear Latent Variable Models},
year = {2019} }
TY - EJOUR
T1 - ENLLVM: Ensemble based Nonlinear Bayesian Filtering using Linear Latent Variable Models
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4128
ER -
. (2019). ENLLVM: Ensemble based Nonlinear Bayesian Filtering using Linear Latent Variable Models. IEEE SigPort. http://sigport.org/4128
, 2019. ENLLVM: Ensemble based Nonlinear Bayesian Filtering using Linear Latent Variable Models. Available at: http://sigport.org/4128.
. (2019). "ENLLVM: Ensemble based Nonlinear Bayesian Filtering using Linear Latent Variable Models." Web.
1. . ENLLVM: Ensemble based Nonlinear Bayesian Filtering using Linear Latent Variable Models [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4128

Variational and Hierarchical Recurrent Autoencoder

Jen-Tzung Chien

Despite a great success in learning representation for image data, it is challenging to learn the stochastic latent features from natural language based on variational inference. The difficulty in stochastic sequential learning is due to the posterior collapse caused by an autoregressive decoder which is prone to be too strong to learn sufficient latent information during optimization. To compensate this weakness in learning procedure, a sophisticated latent structure is required to assure good convergence so that random features are sufficiently captured for sequential decoding.

Paper Details

Authors:
Jen-Tzung Chien
Jen-Tzung Chien and Chun-Wei Wang
Submitted On:
7 May 2019 - 8:19pm
Short Link:
Type:
Event:
Paper Code:
Document Year:
Cite

Document Files

[ICASSP 2019] Variational and hierarchical recurrent autoencoder.pdf

(19)

Icassp19_hier.pdf

(19)

Keywords

Subscribe

Send Author a Private Message
[1] Jen-Tzung Chien and Chun-Wei Wang, "Variational and Hierarchical Recurrent Autoencoder", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3968. Accessed: Jul. 19, 2019.
@article{3968-19,
url = {http://sigport.org/3968},
author = {Jen-Tzung Chien and Chun-Wei Wang },
publisher = {IEEE SigPort},
title = {Variational and Hierarchical Recurrent Autoencoder},
year = {2019} }
TY - EJOUR
T1 - Variational and Hierarchical Recurrent Autoencoder
AU - Jen-Tzung Chien and Chun-Wei Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3968
ER -
Jen-Tzung Chien and Chun-Wei Wang. (2019). Variational and Hierarchical Recurrent Autoencoder. IEEE SigPort. http://sigport.org/3968
Jen-Tzung Chien and Chun-Wei Wang, 2019. Variational and Hierarchical Recurrent Autoencoder. Available at: http://sigport.org/3968.
Jen-Tzung Chien and Chun-Wei Wang. (2019). "Variational and Hierarchical Recurrent Autoencoder." Web.
1. Jen-Tzung Chien and Chun-Wei Wang. Variational and Hierarchical Recurrent Autoencoder [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3968

A Learning Approach for Optimal Codebook Selection in Spatial Modulation Systems

Vidit Saxena

In spatial modulation (SM) systems that utilize multiple transmit antennas/patterns with a single radio front-end, we propose a learning approach to predict the average symbol error rate (SER) conditioned on the instantaneous channel state. We show that the predicted SER can be used to lower the average SER over Rayleigh fading channels by selecting the optimal codebook in each transmission instance.

Paper Details

Authors:
Vidit Saxena
Baptiste Cavarec, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson
Submitted On:
27 March 2019 - 9:05am
Short Link:
Type:
Document Year:
Cite

Document Files

Asilomar18_Poster_AdaptiveSM

(24)

Keywords

Subscribe

Send Author a Private Message
[1] Baptiste Cavarec, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson, "A Learning Approach for Optimal Codebook Selection in Spatial Modulation Systems", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3669. Accessed: Jul. 19, 2019.
@article{3669-18,
url = {http://sigport.org/3669},
author = {Baptiste Cavarec; Joakim Jaldén; Hugo Tullberg; Mats Bengtsson },
publisher = {IEEE SigPort},
title = {A Learning Approach for Optimal Codebook Selection in Spatial Modulation Systems},
year = {2018} }
TY - EJOUR
T1 - A Learning Approach for Optimal Codebook Selection in Spatial Modulation Systems
AU - Baptiste Cavarec; Joakim Jaldén; Hugo Tullberg; Mats Bengtsson
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3669
ER -
Baptiste Cavarec, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson. (2018). A Learning Approach for Optimal Codebook Selection in Spatial Modulation Systems. IEEE SigPort. http://sigport.org/3669
Baptiste Cavarec, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson, 2018. A Learning Approach for Optimal Codebook Selection in Spatial Modulation Systems. Available at: http://sigport.org/3669.
Baptiste Cavarec, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson. (2018). "A Learning Approach for Optimal Codebook Selection in Spatial Modulation Systems." Web.
1. Baptiste Cavarec, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson. A Learning Approach for Optimal Codebook Selection in Spatial Modulation Systems [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3669

Bayesian Learning based Millimeter-Wave Sparse Channel Estimation with Hybrid Antenna Arrays

Markku Juntti

We consider the problem of millimeter-wave (mmWave) channel estimation with a hybrid digital-analog two-stage beamforming structure. A radio frequency (RF) chain excites a dedicated set of antenna subarrays. To compensate for the severe path loss, known training signals are beamformed and swept to scan the angular space. Since the mmWave channels typically exhibit sparsity, the channel response can usually be expressed as a linear combination of a small number of scattering clusters.

Paper Details

Authors:
Markku Juntti
Mubarak Umar Aminu, Marian Codreanu
Submitted On:
21 June 2018 - 9:28am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

spawc-poster .pdf

(24)

Keywords

Subscribe

Send Author a Private Message
[1] Mubarak Umar Aminu, Marian Codreanu, "Bayesian Learning based Millimeter-Wave Sparse Channel Estimation with Hybrid Antenna Arrays", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3284. Accessed: Jul. 19, 2019.
@article{3284-18,
url = {http://sigport.org/3284},
author = {Mubarak Umar Aminu; Marian Codreanu },
publisher = {IEEE SigPort},
title = {Bayesian Learning based Millimeter-Wave Sparse Channel Estimation with Hybrid Antenna Arrays},
year = {2018} }
TY - EJOUR
T1 - Bayesian Learning based Millimeter-Wave Sparse Channel Estimation with Hybrid Antenna Arrays
AU - Mubarak Umar Aminu; Marian Codreanu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3284
ER -
Mubarak Umar Aminu, Marian Codreanu. (2018). Bayesian Learning based Millimeter-Wave Sparse Channel Estimation with Hybrid Antenna Arrays. IEEE SigPort. http://sigport.org/3284
Mubarak Umar Aminu, Marian Codreanu, 2018. Bayesian Learning based Millimeter-Wave Sparse Channel Estimation with Hybrid Antenna Arrays. Available at: http://sigport.org/3284.
Mubarak Umar Aminu, Marian Codreanu. (2018). "Bayesian Learning based Millimeter-Wave Sparse Channel Estimation with Hybrid Antenna Arrays." Web.
1. Mubarak Umar Aminu, Marian Codreanu. Bayesian Learning based Millimeter-Wave Sparse Channel Estimation with Hybrid Antenna Arrays [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3284

CLUSTERING-GUIDED GP-UCB FOR BAYESIAN OPTIMIZATION

Jungtaek Kim

Bayesian optimization is a powerful technique for finding extrema of an objective function, a closed-form expression of which is not given but expensive evaluations at query points are available. Gaussian Process (GP) regression is often used to estimate the objective function and uncertainty estimates that guide GP-Upper Confidence Bound (GP-UCB) to determine where next to sample from the objective function, balancing exploration and exploitation. In general, it requires an auxiliary optimization to tune the hyperparameter in GP-UCB, which is sometimes not easy to carry out in practice.

Paper Details

Authors:
Jungtaek Kim
Jungtaek Kim, Seungjin Choi
Submitted On:
12 April 2018 - 4:15pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster_icassp_2018.pdf

(324)

Keywords

Subscribe

Send Author a Private Message
[1] Jungtaek Kim, Seungjin Choi, "CLUSTERING-GUIDED GP-UCB FOR BAYESIAN OPTIMIZATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2492. Accessed: Jul. 19, 2019.
@article{2492-18,
url = {http://sigport.org/2492},
author = {Jungtaek Kim; Seungjin Choi },
publisher = {IEEE SigPort},
title = {CLUSTERING-GUIDED GP-UCB FOR BAYESIAN OPTIMIZATION},
year = {2018} }
TY - EJOUR
T1 - CLUSTERING-GUIDED GP-UCB FOR BAYESIAN OPTIMIZATION
AU - Jungtaek Kim; Seungjin Choi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2492
ER -
Jungtaek Kim, Seungjin Choi. (2018). CLUSTERING-GUIDED GP-UCB FOR BAYESIAN OPTIMIZATION. IEEE SigPort. http://sigport.org/2492
Jungtaek Kim, Seungjin Choi, 2018. CLUSTERING-GUIDED GP-UCB FOR BAYESIAN OPTIMIZATION. Available at: http://sigport.org/2492.
Jungtaek Kim, Seungjin Choi. (2018). "CLUSTERING-GUIDED GP-UCB FOR BAYESIAN OPTIMIZATION." Web.
1. Jungtaek Kim, Seungjin Choi. CLUSTERING-GUIDED GP-UCB FOR BAYESIAN OPTIMIZATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2492

INFORMATION FUSION USING PARTICLES INTERSECTION

Or Tzlil

Paper Details

Authors:
Or Tzlil
Or Tzlil, Avishy Carmi
Submitted On:
12 April 2018 - 11:16am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Information Fusion using Particle Intersection.pdf

(29)

Keywords

Additional Categories

Subscribe

Send Author a Private Message
[1] Or Tzlil, Avishy Carmi, "INFORMATION FUSION USING PARTICLES INTERSECTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2385. Accessed: Jul. 19, 2019.
@article{2385-18,
url = {http://sigport.org/2385},
author = {Or Tzlil; Avishy Carmi },
publisher = {IEEE SigPort},
title = {INFORMATION FUSION USING PARTICLES INTERSECTION},
year = {2018} }
TY - EJOUR
T1 - INFORMATION FUSION USING PARTICLES INTERSECTION
AU - Or Tzlil; Avishy Carmi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2385
ER -
Or Tzlil, Avishy Carmi. (2018). INFORMATION FUSION USING PARTICLES INTERSECTION. IEEE SigPort. http://sigport.org/2385
Or Tzlil, Avishy Carmi, 2018. INFORMATION FUSION USING PARTICLES INTERSECTION. Available at: http://sigport.org/2385.
Or Tzlil, Avishy Carmi. (2018). "INFORMATION FUSION USING PARTICLES INTERSECTION." Web.
1. Or Tzlil, Avishy Carmi. INFORMATION FUSION USING PARTICLES INTERSECTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2385

Pages