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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: Nov. 16, 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

Deep-learning-based pipe leak detection using image-based leak features

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Authors:
Doo-Byung Yoon, Se Won OH, Gwan Joong Kim, Nae-Soo Kim, Cheol-Sig Pyo
Submitted On:
8 October 2018 - 11:29am
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Poster_ICIP2018.pdf

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[1] Doo-Byung Yoon, Se Won OH, Gwan Joong Kim, Nae-Soo Kim, Cheol-Sig Pyo, "Deep-learning-based pipe leak detection using image-based leak features", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3637. Accessed: Nov. 16, 2018.
@article{3637-18,
url = {http://sigport.org/3637},
author = {Doo-Byung Yoon; Se Won OH; Gwan Joong Kim; Nae-Soo Kim; Cheol-Sig Pyo },
publisher = {IEEE SigPort},
title = {Deep-learning-based pipe leak detection using image-based leak features},
year = {2018} }
TY - EJOUR
T1 - Deep-learning-based pipe leak detection using image-based leak features
AU - Doo-Byung Yoon; Se Won OH; Gwan Joong Kim; Nae-Soo Kim; Cheol-Sig Pyo
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3637
ER -
Doo-Byung Yoon, Se Won OH, Gwan Joong Kim, Nae-Soo Kim, Cheol-Sig Pyo. (2018). Deep-learning-based pipe leak detection using image-based leak features. IEEE SigPort. http://sigport.org/3637
Doo-Byung Yoon, Se Won OH, Gwan Joong Kim, Nae-Soo Kim, Cheol-Sig Pyo, 2018. Deep-learning-based pipe leak detection using image-based leak features. Available at: http://sigport.org/3637.
Doo-Byung Yoon, Se Won OH, Gwan Joong Kim, Nae-Soo Kim, Cheol-Sig Pyo. (2018). "Deep-learning-based pipe leak detection using image-based leak features." Web.
1. Doo-Byung Yoon, Se Won OH, Gwan Joong Kim, Nae-Soo Kim, Cheol-Sig Pyo. Deep-learning-based pipe leak detection using image-based leak features [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3637

Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information

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Authors:
Ji-Hoon Bae, Junho Yim, Nae-Soo Kim, Cheol-Sig Pyo, Junmo Kim
Submitted On:
8 October 2018 - 11:18am
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ICIP_DoyeobYeo.pdf

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[1] Ji-Hoon Bae, Junho Yim, Nae-Soo Kim, Cheol-Sig Pyo, Junmo Kim, "Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3636. Accessed: Nov. 16, 2018.
@article{3636-18,
url = {http://sigport.org/3636},
author = {Ji-Hoon Bae; Junho Yim; Nae-Soo Kim; Cheol-Sig Pyo; Junmo Kim },
publisher = {IEEE SigPort},
title = {Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information},
year = {2018} }
TY - EJOUR
T1 - Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information
AU - Ji-Hoon Bae; Junho Yim; Nae-Soo Kim; Cheol-Sig Pyo; Junmo Kim
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3636
ER -
Ji-Hoon Bae, Junho Yim, Nae-Soo Kim, Cheol-Sig Pyo, Junmo Kim. (2018). Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information. IEEE SigPort. http://sigport.org/3636
Ji-Hoon Bae, Junho Yim, Nae-Soo Kim, Cheol-Sig Pyo, Junmo Kim, 2018. Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information. Available at: http://sigport.org/3636.
Ji-Hoon Bae, Junho Yim, Nae-Soo Kim, Cheol-Sig Pyo, Junmo Kim. (2018). "Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information." Web.
1. Ji-Hoon Bae, Junho Yim, Nae-Soo Kim, Cheol-Sig Pyo, Junmo Kim. Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3636

AN INTERACTIVE CONTENT-BASED 3D SHAPE RETRIEVAL SYSTEM FOR ON-SITE CULTURAL HERITAGE ANALYSIS


In this paper, we analyse the process of designing a Content-
Based 3D shape Retrieval (CB3DR) adapted for non-experts.
Our CB3DR solution aims at scanning an object on the fly
with a low-cost 3D sensor and retrieve similar shapes from
a database using the 3D point cloud acquired. Our system
should meet the requirements of archaeologists who would
like to be able to acquire artefacts without prior expertise in
scanning, then query easily from the field knowledge bases
for Cultural Heritage, and thus retrieve artefacts (i.e. objects

Icip2018.pptx

File Icip2018.pptx (14 downloads)

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Authors:
Lirone Samoun, Thomas Fisichella, Diane Lingrand, Lucas Malleus, Frederic Precioso
Submitted On:
12 October 2018 - 5:10am
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Icip2018.pptx

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[1] Lirone Samoun, Thomas Fisichella, Diane Lingrand, Lucas Malleus, Frederic Precioso, "AN INTERACTIVE CONTENT-BASED 3D SHAPE RETRIEVAL SYSTEM FOR ON-SITE CULTURAL HERITAGE ANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3595. Accessed: Nov. 16, 2018.
@article{3595-18,
url = {http://sigport.org/3595},
author = {Lirone Samoun; Thomas Fisichella; Diane Lingrand; Lucas Malleus; Frederic Precioso },
publisher = {IEEE SigPort},
title = {AN INTERACTIVE CONTENT-BASED 3D SHAPE RETRIEVAL SYSTEM FOR ON-SITE CULTURAL HERITAGE ANALYSIS},
year = {2018} }
TY - EJOUR
T1 - AN INTERACTIVE CONTENT-BASED 3D SHAPE RETRIEVAL SYSTEM FOR ON-SITE CULTURAL HERITAGE ANALYSIS
AU - Lirone Samoun; Thomas Fisichella; Diane Lingrand; Lucas Malleus; Frederic Precioso
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3595
ER -
Lirone Samoun, Thomas Fisichella, Diane Lingrand, Lucas Malleus, Frederic Precioso. (2018). AN INTERACTIVE CONTENT-BASED 3D SHAPE RETRIEVAL SYSTEM FOR ON-SITE CULTURAL HERITAGE ANALYSIS. IEEE SigPort. http://sigport.org/3595
Lirone Samoun, Thomas Fisichella, Diane Lingrand, Lucas Malleus, Frederic Precioso, 2018. AN INTERACTIVE CONTENT-BASED 3D SHAPE RETRIEVAL SYSTEM FOR ON-SITE CULTURAL HERITAGE ANALYSIS. Available at: http://sigport.org/3595.
Lirone Samoun, Thomas Fisichella, Diane Lingrand, Lucas Malleus, Frederic Precioso. (2018). "AN INTERACTIVE CONTENT-BASED 3D SHAPE RETRIEVAL SYSTEM FOR ON-SITE CULTURAL HERITAGE ANALYSIS." Web.
1. Lirone Samoun, Thomas Fisichella, Diane Lingrand, Lucas Malleus, Frederic Precioso. AN INTERACTIVE CONTENT-BASED 3D SHAPE RETRIEVAL SYSTEM FOR ON-SITE CULTURAL HERITAGE ANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3595

BOTTOM-UP ATTENTION GUIDANCE FOR RECURRENT IMAGE RECOGNITION

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Authors:
Hamed R. Tavakoli, Ali Borji, Rao M. Anwer, Esa Rahtu, Juho Kannala
Submitted On:
5 October 2018 - 4:04am
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ICIP.pdf

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[1] Hamed R. Tavakoli, Ali Borji, Rao M. Anwer, Esa Rahtu, Juho Kannala, "BOTTOM-UP ATTENTION GUIDANCE FOR RECURRENT IMAGE RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3517. Accessed: Nov. 16, 2018.
@article{3517-18,
url = {http://sigport.org/3517},
author = {Hamed R. Tavakoli; Ali Borji; Rao M. Anwer; Esa Rahtu; Juho Kannala },
publisher = {IEEE SigPort},
title = {BOTTOM-UP ATTENTION GUIDANCE FOR RECURRENT IMAGE RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - BOTTOM-UP ATTENTION GUIDANCE FOR RECURRENT IMAGE RECOGNITION
AU - Hamed R. Tavakoli; Ali Borji; Rao M. Anwer; Esa Rahtu; Juho Kannala
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3517
ER -
Hamed R. Tavakoli, Ali Borji, Rao M. Anwer, Esa Rahtu, Juho Kannala. (2018). BOTTOM-UP ATTENTION GUIDANCE FOR RECURRENT IMAGE RECOGNITION. IEEE SigPort. http://sigport.org/3517
Hamed R. Tavakoli, Ali Borji, Rao M. Anwer, Esa Rahtu, Juho Kannala, 2018. BOTTOM-UP ATTENTION GUIDANCE FOR RECURRENT IMAGE RECOGNITION. Available at: http://sigport.org/3517.
Hamed R. Tavakoli, Ali Borji, Rao M. Anwer, Esa Rahtu, Juho Kannala. (2018). "BOTTOM-UP ATTENTION GUIDANCE FOR RECURRENT IMAGE RECOGNITION." Web.
1. Hamed R. Tavakoli, Ali Borji, Rao M. Anwer, Esa Rahtu, Juho Kannala. BOTTOM-UP ATTENTION GUIDANCE FOR RECURRENT IMAGE RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3517

GRADIENT BASED EVOLUTION TO OPTIMIZE THE STRUCTURE OF CONVOLUTIONAL NEURAL NETWORKS

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Submitted On:
4 October 2018 - 9:27am
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[1] , "GRADIENT BASED EVOLUTION TO OPTIMIZE THE STRUCTURE OF CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3401. Accessed: Nov. 16, 2018.
@article{3401-18,
url = {http://sigport.org/3401},
author = { },
publisher = {IEEE SigPort},
title = {GRADIENT BASED EVOLUTION TO OPTIMIZE THE STRUCTURE OF CONVOLUTIONAL NEURAL NETWORKS},
year = {2018} }
TY - EJOUR
T1 - GRADIENT BASED EVOLUTION TO OPTIMIZE THE STRUCTURE OF CONVOLUTIONAL NEURAL NETWORKS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3401
ER -
. (2018). GRADIENT BASED EVOLUTION TO OPTIMIZE THE STRUCTURE OF CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/3401
, 2018. GRADIENT BASED EVOLUTION TO OPTIMIZE THE STRUCTURE OF CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/3401.
. (2018). "GRADIENT BASED EVOLUTION TO OPTIMIZE THE STRUCTURE OF CONVOLUTIONAL NEURAL NETWORKS." Web.
1. . GRADIENT BASED EVOLUTION TO OPTIMIZE THE STRUCTURE OF CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3401

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: Nov. 16, 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.

poster_v.pdf

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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: Nov. 16, 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
Submitted On:
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: Nov. 16, 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: Nov. 16, 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

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