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Emerging: Big Data

GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)

Nicholas Sidiropoulos

We reveal an interesting link between tensors and multivariate statistics. The rank of a multivariate probability tensor can be interpreted as a nonlinear measure of statistical dependence of the associated random variables. Rank equals one when the random variables are independent, and complete statistical dependence corresponds to full rank; but we show that rank as low as two can already model strong statistical dependence.

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Authors:
Nicholas Sidiropoulos
N.D. Sidiropoulos, N. Kargas, X. Fu
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24 December 2018 - 8:25pm
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GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)

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[1] N.D. Sidiropoulos, N. Kargas, X. Fu, "GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3842. Accessed: Mar. 23, 2019.
@article{3842-18,
url = {http://sigport.org/3842},
author = {N.D. Sidiropoulos; N. Kargas; X. Fu },
publisher = {IEEE SigPort},
title = {GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)},
year = {2018} }
TY - EJOUR
T1 - GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)
AU - N.D. Sidiropoulos; N. Kargas; X. Fu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3842
ER -
N.D. Sidiropoulos, N. Kargas, X. Fu. (2018). GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu). IEEE SigPort. http://sigport.org/3842
N.D. Sidiropoulos, N. Kargas, X. Fu, 2018. GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu). Available at: http://sigport.org/3842.
N.D. Sidiropoulos, N. Kargas, X. Fu. (2018). "GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)." Web.
1. N.D. Sidiropoulos, N. Kargas, X. Fu. GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu) [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3842

Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs

Anuva Kulkarni

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Authors:
Anuva Kulkarni
Anuva Kulkarni, Franz Franchetti, Jelena Kovacevic
Submitted On:
27 November 2018 - 1:27am
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poster_globalsip_v2.pdf

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[1] Anuva Kulkarni, Franz Franchetti, Jelena Kovacevic, "Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3804. Accessed: Mar. 23, 2019.
@article{3804-18,
url = {http://sigport.org/3804},
author = {Anuva Kulkarni; Franz Franchetti; Jelena Kovacevic },
publisher = {IEEE SigPort},
title = {Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs},
year = {2018} }
TY - EJOUR
T1 - Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs
AU - Anuva Kulkarni; Franz Franchetti; Jelena Kovacevic
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3804
ER -
Anuva Kulkarni, Franz Franchetti, Jelena Kovacevic. (2018). Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs. IEEE SigPort. http://sigport.org/3804
Anuva Kulkarni, Franz Franchetti, Jelena Kovacevic, 2018. Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs. Available at: http://sigport.org/3804.
Anuva Kulkarni, Franz Franchetti, Jelena Kovacevic. (2018). "Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs." Web.
1. Anuva Kulkarni, Franz Franchetti, Jelena Kovacevic. Large-Scale Algorithm Design for Parallel FFT-based Simulations on GPUs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3804

Semi-Supervised Clustering Based on Signed Total Variation

Peter Berger

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Authors:
Peter Berger
Peter Berger, Thomas Dittrich, Gerald Matz
Submitted On:
21 November 2018 - 4:28am
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PosterGlobalSip.pdf

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[1] Peter Berger, Thomas Dittrich, Gerald Matz, "Semi-Supervised Clustering Based on Signed Total Variation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3697. Accessed: Mar. 23, 2019.
@article{3697-18,
url = {http://sigport.org/3697},
author = {Peter Berger; Thomas Dittrich; Gerald Matz },
publisher = {IEEE SigPort},
title = {Semi-Supervised Clustering Based on Signed Total Variation},
year = {2018} }
TY - EJOUR
T1 - Semi-Supervised Clustering Based on Signed Total Variation
AU - Peter Berger; Thomas Dittrich; Gerald Matz
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3697
ER -
Peter Berger, Thomas Dittrich, Gerald Matz. (2018). Semi-Supervised Clustering Based on Signed Total Variation. IEEE SigPort. http://sigport.org/3697
Peter Berger, Thomas Dittrich, Gerald Matz, 2018. Semi-Supervised Clustering Based on Signed Total Variation. Available at: http://sigport.org/3697.
Peter Berger, Thomas Dittrich, Gerald Matz. (2018). "Semi-Supervised Clustering Based on Signed Total Variation." Web.
1. Peter Berger, Thomas Dittrich, Gerald Matz. Semi-Supervised Clustering Based on Signed Total Variation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3697

SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU

Koichi Shirahata

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Authors:
Koichi Shirahata
Submitted On:
8 October 2018 - 5:17am
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icip2018_oral_shirahata.pdf

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[1] , "SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3598. Accessed: Mar. 23, 2019.
@article{3598-18,
url = {http://sigport.org/3598},
author = { },
publisher = {IEEE SigPort},
title = {SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU},
year = {2018} }
TY - EJOUR
T1 - SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3598
ER -
. (2018). SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU. IEEE SigPort. http://sigport.org/3598
, 2018. SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU. Available at: http://sigport.org/3598.
. (2018). "SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU." Web.
1. . SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3598

Performance analysis of distributed radio interferometric calibration

Sarod Yatawatta

Distributed calibration based on consensus optimization is a computationally efficient method to calibrate large radio interferometers such as LOFAR and SKA. Calibrating along multiple directions in the sky and removing the bright foreground signal is a crucial step in many science cases in radio interferometry. The residual data contain weak signals of huge scientific interest and of particular concern is the effect of incomplete sky models used in calibration on the residual. In order to study this, we consider the mapping between the input uncalibrated data and the output residual data.

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Authors:
Sarod Yatawatta
Submitted On:
17 July 2018 - 6:22am
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lofar75.pdf

lofar75.pdf

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[1] , "Performance analysis of distributed radio interferometric calibration", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3358. Accessed: Mar. 23, 2019.
@article{3358-18,
url = {http://sigport.org/3358},
author = { },
publisher = {IEEE SigPort},
title = {Performance analysis of distributed radio interferometric calibration},
year = {2018} }
TY - EJOUR
T1 - Performance analysis of distributed radio interferometric calibration
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3358
ER -
. (2018). Performance analysis of distributed radio interferometric calibration. IEEE SigPort. http://sigport.org/3358
, 2018. Performance analysis of distributed radio interferometric calibration. Available at: http://sigport.org/3358.
. (2018). "Performance analysis of distributed radio interferometric calibration." Web.
1. . Performance analysis of distributed radio interferometric calibration [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3358

Multi-scale algorithms for optimal transport

Bernhard Schmitzer

Optimal transport is a geometrically intuitive and robust way to quantify differences between probability measures.
It is becoming increasingly popular as numerical tool in image processing, computer vision and machine learning.
A key challenge is its efficient computation, in particular on large problems. Various algorithms exist, tailored to different special cases.
Multi-scale methods can be applied to classical discrete algorithms, as well as entropy regularization techniques. They provide a good compromise between efficiency and flexibility.

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Authors:
Bernhard Schmitzer
Submitted On:
2 June 2018 - 2:58am
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schmitzer_2018-06_Lausanne.pdf

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[1] , "Multi-scale algorithms for optimal transport", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3230. Accessed: Mar. 23, 2019.
@article{3230-18,
url = {http://sigport.org/3230},
author = { },
publisher = {IEEE SigPort},
title = {Multi-scale algorithms for optimal transport},
year = {2018} }
TY - EJOUR
T1 - Multi-scale algorithms for optimal transport
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3230
ER -
. (2018). Multi-scale algorithms for optimal transport. IEEE SigPort. http://sigport.org/3230
, 2018. Multi-scale algorithms for optimal transport. Available at: http://sigport.org/3230.
. (2018). "Multi-scale algorithms for optimal transport." Web.
1. . Multi-scale algorithms for optimal transport [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3230

Vector compression for similarity search using Multi-layer Sparse Ternary Codes

Sohrab Ferdowsi

It was shown recently that Sparse Ternary Codes (STC) posses superior ``coding gain'' compared to the classical binary hashing framework and can successfully be used for large-scale search applications. This work extends the STC for compression and proposes a rate-distortion efficient design. We first study a single-layer setup where we show that binary encoding intrinsically suffers from poor compression quality while STC, thanks to the flexibility in design, can have near-optimal rate allocation. We further show that single-layer codes should be limited to very low rates.

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Authors:
Sohrab Ferdowsi
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov
Submitted On:
1 June 2018 - 12:45pm
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DSW2018_poster.pdf

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[1] Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov, "Vector compression for similarity search using Multi-layer Sparse Ternary Codes", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3229. Accessed: Mar. 23, 2019.
@article{3229-18,
url = {http://sigport.org/3229},
author = {Sohrab Ferdowsi; Slava Voloshynovskiy; Dimche Kostadinov },
publisher = {IEEE SigPort},
title = {Vector compression for similarity search using Multi-layer Sparse Ternary Codes},
year = {2018} }
TY - EJOUR
T1 - Vector compression for similarity search using Multi-layer Sparse Ternary Codes
AU - Sohrab Ferdowsi; Slava Voloshynovskiy; Dimche Kostadinov
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3229
ER -
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov. (2018). Vector compression for similarity search using Multi-layer Sparse Ternary Codes. IEEE SigPort. http://sigport.org/3229
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov, 2018. Vector compression for similarity search using Multi-layer Sparse Ternary Codes. Available at: http://sigport.org/3229.
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov. (2018). "Vector compression for similarity search using Multi-layer Sparse Ternary Codes." Web.
1. Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov. Vector compression for similarity search using Multi-layer Sparse Ternary Codes [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3229

Sparse Subspace Clustering with Missing and Corrupted Data

Zachary Charles

In many settings, we can accurately model high-dimensional data as lying in a union of subspaces. Subspace clustering is the process of inferring the subspaces and determining which point belongs to each subspace. In this paper we study a ro- bust variant of sparse subspace clustering (SSC). While SSC is well-understood when there is little or no noise, less is known about SSC under significant noise or missing en- tries. We establish clustering guarantees in the presence of corrupted or missing entries.

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Authors:
Zachary Charles
Amin Jalali, Rebecca Willett
Submitted On:
31 May 2018 - 6:30pm
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sparse_subpsace_clustering_with_missing_and_corrupted_data.pdf

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[1] Amin Jalali, Rebecca Willett, "Sparse Subspace Clustering with Missing and Corrupted Data", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3227. Accessed: Mar. 23, 2019.
@article{3227-18,
url = {http://sigport.org/3227},
author = {Amin Jalali; Rebecca Willett },
publisher = {IEEE SigPort},
title = {Sparse Subspace Clustering with Missing and Corrupted Data},
year = {2018} }
TY - EJOUR
T1 - Sparse Subspace Clustering with Missing and Corrupted Data
AU - Amin Jalali; Rebecca Willett
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3227
ER -
Amin Jalali, Rebecca Willett. (2018). Sparse Subspace Clustering with Missing and Corrupted Data. IEEE SigPort. http://sigport.org/3227
Amin Jalali, Rebecca Willett, 2018. Sparse Subspace Clustering with Missing and Corrupted Data. Available at: http://sigport.org/3227.
Amin Jalali, Rebecca Willett. (2018). "Sparse Subspace Clustering with Missing and Corrupted Data." Web.
1. Amin Jalali, Rebecca Willett. Sparse Subspace Clustering with Missing and Corrupted Data [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3227

SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION

Keith Knight

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Authors:
Keith Knight
Submitted On:
30 May 2018 - 9:30am
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poster2018.pdf

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[1] , "SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3219. Accessed: Mar. 23, 2019.
@article{3219-18,
url = {http://sigport.org/3219},
author = { },
publisher = {IEEE SigPort},
title = {SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION},
year = {2018} }
TY - EJOUR
T1 - SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3219
ER -
. (2018). SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION. IEEE SigPort. http://sigport.org/3219
, 2018. SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION. Available at: http://sigport.org/3219.
. (2018). "SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION." Web.
1. . SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3219

THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE

Oguzhan Teke

Paper Details

Authors:
Oguzhan Teke
Oguzhan Teke, P. P. Vaidyanathan
Submitted On:
22 April 2018 - 12:23am
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async_updates_icassp_presentation.pdf

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[1] Oguzhan Teke, P. P. Vaidyanathan, "THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3126. Accessed: Mar. 23, 2019.
@article{3126-18,
url = {http://sigport.org/3126},
author = {Oguzhan Teke; P. P. Vaidyanathan },
publisher = {IEEE SigPort},
title = {THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE},
year = {2018} }
TY - EJOUR
T1 - THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE
AU - Oguzhan Teke; P. P. Vaidyanathan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3126
ER -
Oguzhan Teke, P. P. Vaidyanathan. (2018). THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE. IEEE SigPort. http://sigport.org/3126
Oguzhan Teke, P. P. Vaidyanathan, 2018. THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE. Available at: http://sigport.org/3126.
Oguzhan Teke, P. P. Vaidyanathan. (2018). "THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE." Web.
1. Oguzhan Teke, P. P. Vaidyanathan. THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3126

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