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Emerging: Big Data

THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE

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Authors:
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: May. 24, 2018.
@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

EXTENDABLE NEURAL MATRIX COMPLETION

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20 April 2018 - 4:21am
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ICASSP-MC-poster.pdf

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[1] , "EXTENDABLE NEURAL MATRIX COMPLETION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3094. Accessed: May. 24, 2018.
@article{3094-18,
url = {http://sigport.org/3094},
author = { },
publisher = {IEEE SigPort},
title = {EXTENDABLE NEURAL MATRIX COMPLETION},
year = {2018} }
TY - EJOUR
T1 - EXTENDABLE NEURAL MATRIX COMPLETION
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3094
ER -
. (2018). EXTENDABLE NEURAL MATRIX COMPLETION. IEEE SigPort. http://sigport.org/3094
, 2018. EXTENDABLE NEURAL MATRIX COMPLETION. Available at: http://sigport.org/3094.
. (2018). "EXTENDABLE NEURAL MATRIX COMPLETION." Web.
1. . EXTENDABLE NEURAL MATRIX COMPLETION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3094

Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing


Wireless distributed computing presents new opportunities to execute intelligent tasks on mobile devices for low-latency applications, by wirelessly aggregating the computation and storage resources among mobile devices. However, for low-latency applications, the key bottleneck lies in the exchange of intermediate results among mobile devices for data shuffling. To improve communication efficiency therein, we establish a novel interference alignment condition by exploiting the locally computed intermediate values as side information.

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Authors:
Yuanming Shi, Zhi Ding
Submitted On:
20 April 2018 - 1:48am
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ICASSP_poster.pdf

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[1] Yuanming Shi, Zhi Ding, "Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3080. Accessed: May. 24, 2018.
@article{3080-18,
url = {http://sigport.org/3080},
author = {Yuanming Shi; Zhi Ding },
publisher = {IEEE SigPort},
title = {Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing},
year = {2018} }
TY - EJOUR
T1 - Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing
AU - Yuanming Shi; Zhi Ding
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3080
ER -
Yuanming Shi, Zhi Ding. (2018). Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing. IEEE SigPort. http://sigport.org/3080
Yuanming Shi, Zhi Ding, 2018. Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing. Available at: http://sigport.org/3080.
Yuanming Shi, Zhi Ding. (2018). "Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing." Web.
1. Yuanming Shi, Zhi Ding. Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3080

Twitter User Geolocation using Multivew Deep Learning


Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows either a content-based or a network-based approach. The former is based on user-generated content while the latter exploits the structure of the network of users. In this paper, we propose a more generic approach, which incorporates not only both content-based and network-based features, but also other available information into a unified model.

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20 April 2018 - 4:22am
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icassp_2018_twitter.pdf

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icassp_2018_twitter.pdf

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[1] , "Twitter User Geolocation using Multivew Deep Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3033. Accessed: May. 24, 2018.
@article{3033-18,
url = {http://sigport.org/3033},
author = { },
publisher = {IEEE SigPort},
title = {Twitter User Geolocation using Multivew Deep Learning},
year = {2018} }
TY - EJOUR
T1 - Twitter User Geolocation using Multivew Deep Learning
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3033
ER -
. (2018). Twitter User Geolocation using Multivew Deep Learning. IEEE SigPort. http://sigport.org/3033
, 2018. Twitter User Geolocation using Multivew Deep Learning. Available at: http://sigport.org/3033.
. (2018). "Twitter User Geolocation using Multivew Deep Learning." Web.
1. . Twitter User Geolocation using Multivew Deep Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3033

Distributed coupled learning over adaptive networks


This work develops an effective distributed algorithm for the solution of stochastic optimization problems that involve partial coupling among both local constraints and local cost functions. While the collection of networked agents is interested in discovering a global model, the individual agents are sensing data that is only dependent on parts of the model. Moreover, different agents may be dependent on different subsets of the model. In this way, cooperation is justified and also necessary to enable recovery of the global information.

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Authors:
Ali H. Sayed
Submitted On:
19 April 2018 - 4:35pm
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Poster_ICASSP.pdf

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[1] Ali H. Sayed, "Distributed coupled learning over adaptive networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3021. Accessed: May. 24, 2018.
@article{3021-18,
url = {http://sigport.org/3021},
author = {Ali H. Sayed },
publisher = {IEEE SigPort},
title = {Distributed coupled learning over adaptive networks},
year = {2018} }
TY - EJOUR
T1 - Distributed coupled learning over adaptive networks
AU - Ali H. Sayed
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3021
ER -
Ali H. Sayed. (2018). Distributed coupled learning over adaptive networks. IEEE SigPort. http://sigport.org/3021
Ali H. Sayed, 2018. Distributed coupled learning over adaptive networks. Available at: http://sigport.org/3021.
Ali H. Sayed. (2018). "Distributed coupled learning over adaptive networks." Web.
1. Ali H. Sayed. Distributed coupled learning over adaptive networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3021

Robust Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers


Due to the grow of modern dataset size and the desire to harness computing power of multiple machines, there is a recent surge of interest in the design of distributed machine learning algorithms. However, distributed algorithms are sensitive to Byzantine attackers who can send falsified data to prevent the convergence of algorithms or lead the algorithms to converge to value of the attackers' choice. Our novel algorithm can deal with an arbitrary number of Byzantine attackers.

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Authors:
Lifeng Lai
Submitted On:
14 April 2018 - 12:40am
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ICASSPposter.pdf

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[1] Lifeng Lai, "Robust Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2795. Accessed: May. 24, 2018.
@article{2795-18,
url = {http://sigport.org/2795},
author = {Lifeng Lai },
publisher = {IEEE SigPort},
title = {Robust Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers},
year = {2018} }
TY - EJOUR
T1 - Robust Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers
AU - Lifeng Lai
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2795
ER -
Lifeng Lai. (2018). Robust Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers. IEEE SigPort. http://sigport.org/2795
Lifeng Lai, 2018. Robust Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers. Available at: http://sigport.org/2795.
Lifeng Lai. (2018). "Robust Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers." Web.
1. Lifeng Lai. Robust Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2795

Common and Individual Feature Extraction using Tensor Decompositions: A Remedy for the Curse of Dimensionality?


A novel method for common and individual feature analysis from exceedingly large-scale data is proposed, in order to ensure the tractability of both the computation and storage and thus mitigate the curse of dimensionality, a major bottleneck in modern data science. This is achieved by making use of the inherent redundancy in so-called multi-block data structures, which represent multiple observations of the same phenomenon taken at different times, angles or recording conditions.

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Authors:
Ilia Kisil, Giuseppe G. Calvi, Andrzej Cichocki, Danilo P. Mandic
Submitted On:
13 April 2018 - 2:12pm
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KISIL_ICASSP_2018.pdf

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[1] Ilia Kisil, Giuseppe G. Calvi, Andrzej Cichocki, Danilo P. Mandic, "Common and Individual Feature Extraction using Tensor Decompositions: A Remedy for the Curse of Dimensionality?", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2739. Accessed: May. 24, 2018.
@article{2739-18,
url = {http://sigport.org/2739},
author = {Ilia Kisil; Giuseppe G. Calvi; Andrzej Cichocki; Danilo P. Mandic },
publisher = {IEEE SigPort},
title = {Common and Individual Feature Extraction using Tensor Decompositions: A Remedy for the Curse of Dimensionality?},
year = {2018} }
TY - EJOUR
T1 - Common and Individual Feature Extraction using Tensor Decompositions: A Remedy for the Curse of Dimensionality?
AU - Ilia Kisil; Giuseppe G. Calvi; Andrzej Cichocki; Danilo P. Mandic
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2739
ER -
Ilia Kisil, Giuseppe G. Calvi, Andrzej Cichocki, Danilo P. Mandic. (2018). Common and Individual Feature Extraction using Tensor Decompositions: A Remedy for the Curse of Dimensionality?. IEEE SigPort. http://sigport.org/2739
Ilia Kisil, Giuseppe G. Calvi, Andrzej Cichocki, Danilo P. Mandic, 2018. Common and Individual Feature Extraction using Tensor Decompositions: A Remedy for the Curse of Dimensionality?. Available at: http://sigport.org/2739.
Ilia Kisil, Giuseppe G. Calvi, Andrzej Cichocki, Danilo P. Mandic. (2018). "Common and Individual Feature Extraction using Tensor Decompositions: A Remedy for the Curse of Dimensionality?." Web.
1. Ilia Kisil, Giuseppe G. Calvi, Andrzej Cichocki, Danilo P. Mandic. Common and Individual Feature Extraction using Tensor Decompositions: A Remedy for the Curse of Dimensionality? [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2739

Deep Learning for Joint Source-Channel Coding of Text


We consider the problem of joint source and channel coding of structured data such as natural language over a noisy channel. The typical approach inspired by information theory to this problem involves performing source coding to first compress the text and then channel coding to add robustness while transmitting across the channel; this approach is optimal with arbitrarily large block lengths for discrete memoryless channels.

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Authors:
Nariman Farsad, Milind Rao, and Andrea Goldsmith
Submitted On:
13 April 2018 - 11:22am
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icassp_jointSC_handout.pdf

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[1] Nariman Farsad, Milind Rao, and Andrea Goldsmith, "Deep Learning for Joint Source-Channel Coding of Text", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2717. Accessed: May. 24, 2018.
@article{2717-18,
url = {http://sigport.org/2717},
author = {Nariman Farsad; Milind Rao; and Andrea Goldsmith },
publisher = {IEEE SigPort},
title = {Deep Learning for Joint Source-Channel Coding of Text},
year = {2018} }
TY - EJOUR
T1 - Deep Learning for Joint Source-Channel Coding of Text
AU - Nariman Farsad; Milind Rao; and Andrea Goldsmith
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2717
ER -
Nariman Farsad, Milind Rao, and Andrea Goldsmith. (2018). Deep Learning for Joint Source-Channel Coding of Text. IEEE SigPort. http://sigport.org/2717
Nariman Farsad, Milind Rao, and Andrea Goldsmith, 2018. Deep Learning for Joint Source-Channel Coding of Text. Available at: http://sigport.org/2717.
Nariman Farsad, Milind Rao, and Andrea Goldsmith. (2018). "Deep Learning for Joint Source-Channel Coding of Text." Web.
1. Nariman Farsad, Milind Rao, and Andrea Goldsmith. Deep Learning for Joint Source-Channel Coding of Text [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2717

PRIMA: PROBABILISTIC RANKING WITH INTER-ITEM COMPETITION AND MULTI-ATTRIBUTE UTILITY FUNCTION

Paper Details

Authors:
Qingming Li, Zhanjiang Chen, H. Vicky Zhao, Yan Lindsay Sun
Submitted On:
13 April 2018 - 4:51am
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PRIMA4.pdf

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[1] Qingming Li, Zhanjiang Chen, H. Vicky Zhao, Yan Lindsay Sun, "PRIMA: PROBABILISTIC RANKING WITH INTER-ITEM COMPETITION AND MULTI-ATTRIBUTE UTILITY FUNCTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2656. Accessed: May. 24, 2018.
@article{2656-18,
url = {http://sigport.org/2656},
author = {Qingming Li; Zhanjiang Chen; H. Vicky Zhao; Yan Lindsay Sun },
publisher = {IEEE SigPort},
title = {PRIMA: PROBABILISTIC RANKING WITH INTER-ITEM COMPETITION AND MULTI-ATTRIBUTE UTILITY FUNCTION},
year = {2018} }
TY - EJOUR
T1 - PRIMA: PROBABILISTIC RANKING WITH INTER-ITEM COMPETITION AND MULTI-ATTRIBUTE UTILITY FUNCTION
AU - Qingming Li; Zhanjiang Chen; H. Vicky Zhao; Yan Lindsay Sun
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2656
ER -
Qingming Li, Zhanjiang Chen, H. Vicky Zhao, Yan Lindsay Sun. (2018). PRIMA: PROBABILISTIC RANKING WITH INTER-ITEM COMPETITION AND MULTI-ATTRIBUTE UTILITY FUNCTION. IEEE SigPort. http://sigport.org/2656
Qingming Li, Zhanjiang Chen, H. Vicky Zhao, Yan Lindsay Sun, 2018. PRIMA: PROBABILISTIC RANKING WITH INTER-ITEM COMPETITION AND MULTI-ATTRIBUTE UTILITY FUNCTION. Available at: http://sigport.org/2656.
Qingming Li, Zhanjiang Chen, H. Vicky Zhao, Yan Lindsay Sun. (2018). "PRIMA: PROBABILISTIC RANKING WITH INTER-ITEM COMPETITION AND MULTI-ATTRIBUTE UTILITY FUNCTION." Web.
1. Qingming Li, Zhanjiang Chen, H. Vicky Zhao, Yan Lindsay Sun. PRIMA: PROBABILISTIC RANKING WITH INTER-ITEM COMPETITION AND MULTI-ATTRIBUTE UTILITY FUNCTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2656

Streaming Influence Maximization in Social Networks based on Multi-Action Credit Distribution


In a social network, influence maximization is the problem of identifying a set of users that own the maximum influence ability across the network. In this paper, a novel credit distribution (CD) based model, termed as the multi-action CD (mCD) model, is introduced to quantify the influence ability of each user. Compared to existing models, the new model can work with practical datasets where one type of action is recorded for multiple times. Based on this model, influence maximization is formulated as a submodular maximization problem under a knapsack constraint, which is NP-hard.

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Authors:
Qilian Yu, Hang Li, Yun Liao, Shuguang Cui
Submitted On:
12 April 2018 - 4:47pm
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ICASSP Poster.pdf

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[1] Qilian Yu, Hang Li, Yun Liao, Shuguang Cui, "Streaming Influence Maximization in Social Networks based on Multi-Action Credit Distribution", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2500. Accessed: May. 24, 2018.
@article{2500-18,
url = {http://sigport.org/2500},
author = {Qilian Yu; Hang Li; Yun Liao; Shuguang Cui },
publisher = {IEEE SigPort},
title = {Streaming Influence Maximization in Social Networks based on Multi-Action Credit Distribution},
year = {2018} }
TY - EJOUR
T1 - Streaming Influence Maximization in Social Networks based on Multi-Action Credit Distribution
AU - Qilian Yu; Hang Li; Yun Liao; Shuguang Cui
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2500
ER -
Qilian Yu, Hang Li, Yun Liao, Shuguang Cui. (2018). Streaming Influence Maximization in Social Networks based on Multi-Action Credit Distribution. IEEE SigPort. http://sigport.org/2500
Qilian Yu, Hang Li, Yun Liao, Shuguang Cui, 2018. Streaming Influence Maximization in Social Networks based on Multi-Action Credit Distribution. Available at: http://sigport.org/2500.
Qilian Yu, Hang Li, Yun Liao, Shuguang Cui. (2018). "Streaming Influence Maximization in Social Networks based on Multi-Action Credit Distribution." Web.
1. Qilian Yu, Hang Li, Yun Liao, Shuguang Cui. Streaming Influence Maximization in Social Networks based on Multi-Action Credit Distribution [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2500

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