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Machine Learning for Signal Processing

Sparse Modeling


Sparse Modeling in Image Processing and Deep LearningSparse approximation is a well-established theory, with a profound impact on the fields of signal and image processing. In this talk we start by presenting this model and its features, and then turn to describe two special cases of it – the convolutional sparse coding (CSC) and its multi-layered version (ML-CSC).  Amazingly, as we will carefully show, ML-CSC provides a solid theoretical foundation to … deep-learning.

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Authors:
Michael Elad
Submitted On:
22 December 2017 - 1:26pm
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ICIP_KeyNote_Talk_small size.pdf

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[1] Michael Elad, "Sparse Modeling ", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2260. Accessed: Jul. 21, 2018.
@article{2260-17,
url = {http://sigport.org/2260},
author = {Michael Elad },
publisher = {IEEE SigPort},
title = {Sparse Modeling },
year = {2017} }
TY - EJOUR
T1 - Sparse Modeling
AU - Michael Elad
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2260
ER -
Michael Elad. (2017). Sparse Modeling . IEEE SigPort. http://sigport.org/2260
Michael Elad, 2017. Sparse Modeling . Available at: http://sigport.org/2260.
Michael Elad. (2017). "Sparse Modeling ." Web.
1. Michael Elad. Sparse Modeling [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2260

Mobile App User Choice Engineering using Behavioral Science Models


When interacting with mobile apps, users need to take decisions and make certain choices out of a set of alternative ones offered by the app. We introduce optimization problems through which we engineer the choices presented to users so that they are nudged towards decisions that lead to better outcomes for them and for the app platform. User decision-making rules are modeled by using principles from behavioral science and machine learning.

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Authors:
Merkourios Karaliopoulos, Iordanis Koutsopoulos
Submitted On:
22 June 2018 - 8:16am
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Poster

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[1] Merkourios Karaliopoulos, Iordanis Koutsopoulos, "Mobile App User Choice Engineering using Behavioral Science Models", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3291. Accessed: Jul. 21, 2018.
@article{3291-18,
url = {http://sigport.org/3291},
author = {Merkourios Karaliopoulos; Iordanis Koutsopoulos },
publisher = {IEEE SigPort},
title = {Mobile App User Choice Engineering using Behavioral Science Models},
year = {2018} }
TY - EJOUR
T1 - Mobile App User Choice Engineering using Behavioral Science Models
AU - Merkourios Karaliopoulos; Iordanis Koutsopoulos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3291
ER -
Merkourios Karaliopoulos, Iordanis Koutsopoulos. (2018). Mobile App User Choice Engineering using Behavioral Science Models. IEEE SigPort. http://sigport.org/3291
Merkourios Karaliopoulos, Iordanis Koutsopoulos, 2018. Mobile App User Choice Engineering using Behavioral Science Models. Available at: http://sigport.org/3291.
Merkourios Karaliopoulos, Iordanis Koutsopoulos. (2018). "Mobile App User Choice Engineering using Behavioral Science Models." Web.
1. Merkourios Karaliopoulos, Iordanis Koutsopoulos. Mobile App User Choice Engineering using Behavioral Science Models [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3291

Communication efficient coreset sampling for distributed learning


In this paper, distributed learning is studied using the approach of coreset. In the context of classification, an algorithm of coreset construction is proposed to reduce the redundancy of data and thus the communication requirement, similarly to source coding in traditional data communications. It is shown that the coreset based boosting has a high convergence rate and small sample complexity. Moreover, it is robust to adversary distribution, thus leading to potential applications in distributed learning systems.

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Authors:
Yawen Fan, Husheng Li
Submitted On:
20 June 2018 - 9:57am
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[1] Yawen Fan, Husheng Li, "Communication efficient coreset sampling for distributed learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3258. Accessed: Jul. 21, 2018.
@article{3258-18,
url = {http://sigport.org/3258},
author = {Yawen Fan; Husheng Li },
publisher = {IEEE SigPort},
title = {Communication efficient coreset sampling for distributed learning},
year = {2018} }
TY - EJOUR
T1 - Communication efficient coreset sampling for distributed learning
AU - Yawen Fan; Husheng Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3258
ER -
Yawen Fan, Husheng Li. (2018). Communication efficient coreset sampling for distributed learning. IEEE SigPort. http://sigport.org/3258
Yawen Fan, Husheng Li, 2018. Communication efficient coreset sampling for distributed learning. Available at: http://sigport.org/3258.
Yawen Fan, Husheng Li. (2018). "Communication efficient coreset sampling for distributed learning." Web.
1. Yawen Fan, Husheng Li. Communication efficient coreset sampling for distributed learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3258

Improved LDA Classifier based on Spiked Models

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Authors:
Houssem Sifaou , Abla Kammoun and Mohamed-Slim Alouini
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20 June 2018 - 9:36am
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[1] Houssem Sifaou , Abla Kammoun and Mohamed-Slim Alouini, "Improved LDA Classifier based on Spiked Models", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3255. Accessed: Jul. 21, 2018.
@article{3255-18,
url = {http://sigport.org/3255},
author = {Houssem Sifaou ; Abla Kammoun and Mohamed-Slim Alouini },
publisher = {IEEE SigPort},
title = {Improved LDA Classifier based on Spiked Models},
year = {2018} }
TY - EJOUR
T1 - Improved LDA Classifier based on Spiked Models
AU - Houssem Sifaou ; Abla Kammoun and Mohamed-Slim Alouini
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3255
ER -
Houssem Sifaou , Abla Kammoun and Mohamed-Slim Alouini. (2018). Improved LDA Classifier based on Spiked Models. IEEE SigPort. http://sigport.org/3255
Houssem Sifaou , Abla Kammoun and Mohamed-Slim Alouini, 2018. Improved LDA Classifier based on Spiked Models. Available at: http://sigport.org/3255.
Houssem Sifaou , Abla Kammoun and Mohamed-Slim Alouini. (2018). "Improved LDA Classifier based on Spiked Models." Web.
1. Houssem Sifaou , Abla Kammoun and Mohamed-Slim Alouini. Improved LDA Classifier based on Spiked Models [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3255

Alternating autoencoders for matrix completion


We consider autoencoders (AEs) for matrix completion (MC) with application to collaborative filtering (CF) for recommedation systems. It is observed that for a given sparse user-item rating matrix, denoted asM, an AE performs matrix factorization so that the recovered matrix is represented as a product of user and item feature matrices.

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Authors:
Kiwon Lee, Yong H. Lee, Changho Suh
Submitted On:
4 June 2018 - 2:48pm
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[1] Kiwon Lee, Yong H. Lee, Changho Suh, "Alternating autoencoders for matrix completion", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3223. Accessed: Jul. 21, 2018.
@article{3223-18,
url = {http://sigport.org/3223},
author = {Kiwon Lee; Yong H. Lee; Changho Suh },
publisher = {IEEE SigPort},
title = {Alternating autoencoders for matrix completion},
year = {2018} }
TY - EJOUR
T1 - Alternating autoencoders for matrix completion
AU - Kiwon Lee; Yong H. Lee; Changho Suh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3223
ER -
Kiwon Lee, Yong H. Lee, Changho Suh. (2018). Alternating autoencoders for matrix completion. IEEE SigPort. http://sigport.org/3223
Kiwon Lee, Yong H. Lee, Changho Suh, 2018. Alternating autoencoders for matrix completion. Available at: http://sigport.org/3223.
Kiwon Lee, Yong H. Lee, Changho Suh. (2018). "Alternating autoencoders for matrix completion." Web.
1. Kiwon Lee, Yong H. Lee, Changho Suh. Alternating autoencoders for matrix completion [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3223

Discriminative Clustering with Cardinality Constraints


Clustering is widely used for exploratory data analysis in a variety of applications. Traditionally clustering is studied as an unsupervised task where no human inputs are provided. A recent trend in clustering is to leverage user provided side information to better infer the clustering structure in data. In this paper, we propose a probabilistic graphical model that allows user to provide as input the desired cluster sizes, namely the cardinality constraints. Our model also incorporates a flexible mechanism to inject control of the crispness of the clusters.

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Authors:
Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern
Submitted On:
25 April 2018 - 2:00pm
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Discriminative Clustering with Cardinality Constraint_ICASSP2018_latest.pdf

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[1] Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern, "Discriminative Clustering with Cardinality Constraints", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3181. Accessed: Jul. 21, 2018.
@article{3181-18,
url = {http://sigport.org/3181},
author = {Anh T. Pham; Raviv Raich; and Xiaoli Z. Fern },
publisher = {IEEE SigPort},
title = {Discriminative Clustering with Cardinality Constraints},
year = {2018} }
TY - EJOUR
T1 - Discriminative Clustering with Cardinality Constraints
AU - Anh T. Pham; Raviv Raich; and Xiaoli Z. Fern
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3181
ER -
Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern. (2018). Discriminative Clustering with Cardinality Constraints. IEEE SigPort. http://sigport.org/3181
Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern, 2018. Discriminative Clustering with Cardinality Constraints. Available at: http://sigport.org/3181.
Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern. (2018). "Discriminative Clustering with Cardinality Constraints." Web.
1. Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern. Discriminative Clustering with Cardinality Constraints [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3181

A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations

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Authors:
Pascal Bianchi, Walid Hachem
Submitted On:
24 April 2018 - 1:14pm
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[1] Pascal Bianchi, Walid Hachem, "A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3170. Accessed: Jul. 21, 2018.
@article{3170-18,
url = {http://sigport.org/3170},
author = {Pascal Bianchi; Walid Hachem },
publisher = {IEEE SigPort},
title = {A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations},
year = {2018} }
TY - EJOUR
T1 - A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations
AU - Pascal Bianchi; Walid Hachem
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3170
ER -
Pascal Bianchi, Walid Hachem. (2018). A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations. IEEE SigPort. http://sigport.org/3170
Pascal Bianchi, Walid Hachem, 2018. A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations. Available at: http://sigport.org/3170.
Pascal Bianchi, Walid Hachem. (2018). "A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations." Web.
1. Pascal Bianchi, Walid Hachem. A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3170

AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO

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Authors:
Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu
Submitted On:
23 April 2018 - 8:52pm
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[1] Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu, "AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3155. Accessed: Jul. 21, 2018.
@article{3155-18,
url = {http://sigport.org/3155},
author = {Alistair Letcher; Jelena Trišović; Collin Cademartori; Xi Chen; Jason Xu },
publisher = {IEEE SigPort},
title = {AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO},
year = {2018} }
TY - EJOUR
T1 - AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO
AU - Alistair Letcher; Jelena Trišović; Collin Cademartori; Xi Chen; Jason Xu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3155
ER -
Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu. (2018). AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO. IEEE SigPort. http://sigport.org/3155
Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu, 2018. AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO. Available at: http://sigport.org/3155.
Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu. (2018). "AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO." Web.
1. Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu. AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3155

Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution


Hyperspectral super-resolution (HSR) is a problem of recovering a high-spectral-spatial-resolution image from a multispectral measurement and a hyperspectral measurement, which have low spectral and spatial resolutions, respectively. We consider a low-rank structured matrix factorization formulation for HSR, which is a non-convex large-scale optimization problem. Our contributions contain both computational and theoretical aspects.

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Authors:
Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu
Submitted On:
19 April 2018 - 10:39pm
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ICASSP 2018 modified.pdf

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[1] Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu, "Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3057. Accessed: Jul. 21, 2018.
@article{3057-18,
url = {http://sigport.org/3057},
author = {Ruiyuan Wu; Chun-Hei Chan; Hoi-To Wai; Wing-Kin Ma; and Xiao Fu },
publisher = {IEEE SigPort},
title = {Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution},
year = {2018} }
TY - EJOUR
T1 - Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution
AU - Ruiyuan Wu; Chun-Hei Chan; Hoi-To Wai; Wing-Kin Ma; and Xiao Fu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3057
ER -
Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu. (2018). Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution. IEEE SigPort. http://sigport.org/3057
Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu, 2018. Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution. Available at: http://sigport.org/3057.
Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu. (2018). "Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution." Web.
1. Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu. Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3057

Language and Noise Transfer in Speech Enhancement Generative Adversarial Network


Speech enhancement deep learning systems usually require large amounts of training data to operate in broad conditions or real applications. This makes the adaptability of those systems into new, low resource environments an important topic. In this work, we present the results of adapting a speech enhancement generative adversarial network by fine-tuning the generator with small amounts of data. We investigate the minimum requirements to obtain a stable behavior in terms of several objective metrics in two very different languages: Catalan and Korean.

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Authors:
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn
Submitted On:
19 April 2018 - 4:44pm
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[1] Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn, "Language and Noise Transfer in Speech Enhancement Generative Adversarial Network", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3025. Accessed: Jul. 21, 2018.
@article{3025-18,
url = {http://sigport.org/3025},
author = {Maruchan Park; Joan Serrà; Antonio Bonafonte; Kang-Hun Ahn },
publisher = {IEEE SigPort},
title = {Language and Noise Transfer in Speech Enhancement Generative Adversarial Network},
year = {2018} }
TY - EJOUR
T1 - Language and Noise Transfer in Speech Enhancement Generative Adversarial Network
AU - Maruchan Park; Joan Serrà; Antonio Bonafonte; Kang-Hun Ahn
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3025
ER -
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn. (2018). Language and Noise Transfer in Speech Enhancement Generative Adversarial Network. IEEE SigPort. http://sigport.org/3025
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn, 2018. Language and Noise Transfer in Speech Enhancement Generative Adversarial Network. Available at: http://sigport.org/3025.
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn. (2018). "Language and Noise Transfer in Speech Enhancement Generative Adversarial Network." Web.
1. Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn. Language and Noise Transfer in Speech Enhancement Generative Adversarial Network [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3025

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