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Data Science Workshop 2018

The 2018 IEEE Data Science Workshop is a new workshop that aims to bring together researchers in academia and industry to share the most recent and exciting advances in data science theory and applications. In particular, the event will gather researchers and practitioners in various academic disciplines of data science, including signal processing, statistics, machine learning, data mining and computer science, along with experts in academic and industrial domains, such as personalized health and medicine, earth and environmental science, applied physics, finance and economics, intelligent manufacturing.

Nonparametric Distributed Detection Using One-Sample Anderson-Darling Test and p-value Fusion


In this paper a method for distributed detection for scenarios when there is no explicit knowledge of the probability models associated with the hypotheses is proposed. The underlying distributions are accurately learned from the data by bootstrapping. We propose using a nonparametric one-sample Anderson-Darling test locally at each sensor. The one-sample version of the test gives superior performance in comparison to the two-sample alternative. The local decision statistics, in particular p-values are then sent to a fusion center to make the final decision.

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Authors:
Topi Halme, Visa Koivunen
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3 June 2018 - 4:59am
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DSW_poster_VK.pdf

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[1] Topi Halme, Visa Koivunen, "Nonparametric Distributed Detection Using One-Sample Anderson-Darling Test and p-value Fusion", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3232. Accessed: Jul. 15, 2018.
@article{3232-18,
url = {http://sigport.org/3232},
author = {Topi Halme; Visa Koivunen },
publisher = {IEEE SigPort},
title = {Nonparametric Distributed Detection Using One-Sample Anderson-Darling Test and p-value Fusion},
year = {2018} }
TY - EJOUR
T1 - Nonparametric Distributed Detection Using One-Sample Anderson-Darling Test and p-value Fusion
AU - Topi Halme; Visa Koivunen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3232
ER -
Topi Halme, Visa Koivunen. (2018). Nonparametric Distributed Detection Using One-Sample Anderson-Darling Test and p-value Fusion. IEEE SigPort. http://sigport.org/3232
Topi Halme, Visa Koivunen, 2018. Nonparametric Distributed Detection Using One-Sample Anderson-Darling Test and p-value Fusion. Available at: http://sigport.org/3232.
Topi Halme, Visa Koivunen. (2018). "Nonparametric Distributed Detection Using One-Sample Anderson-Darling Test and p-value Fusion." Web.
1. Topi Halme, Visa Koivunen. Nonparametric Distributed Detection Using One-Sample Anderson-Darling Test and p-value Fusion [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3232

Multi-scale algorithms for optimal transport


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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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: Jul. 15, 2018.
@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


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, Slava Voloshynovskiy, Dimche Kostadinov
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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: Jul. 15, 2018.
@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

PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING


We apply convolutional neural networks (CNN) for monitoring the
operation of photovoltaic panels. In particular, we predict the daily
electrical power curve of a photovoltaic panel based on the power
curves of neighboring panels. An exceptionally large deviation between
predicted and actual (observed) power curve indicates a malfunctioning
panel. The problem is challenging because the power
curve depends on many factors such as weather conditions and the
surrounding objects causing shadows with a regular time pattern. We

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1 June 2018 - 8:12am
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huuhtanen01.pdf

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[1] , "PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3228. Accessed: Jul. 15, 2018.
@article{3228-18,
url = {http://sigport.org/3228},
author = { },
publisher = {IEEE SigPort},
title = {PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING},
year = {2018} }
TY - EJOUR
T1 - PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3228
ER -
. (2018). PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING. IEEE SigPort. http://sigport.org/3228
, 2018. PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING. Available at: http://sigport.org/3228.
. (2018). "PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING." Web.
1. . PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3228

Sparse Subspace Clustering with Missing and Corrupted Data


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:
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: Jul. 15, 2018.
@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

Convolutional Neural Networks via Node-Varying Graph Filters


Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks. Since two of the key operations that CNNs implement are convolution and pooling, this type of networks is implicitly designed to act on data described by regular structures such as images. Motivated by the recent interest in processing signals defined in irregular domains, we advocate a CNN architecture that operates on signals supported on graphs.

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Authors:
Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro
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31 May 2018 - 7:03pm
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gama-leus-marques-ribeiro-node_variant_graph_filter.pdf

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[1] Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro, "Convolutional Neural Networks via Node-Varying Graph Filters", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3226. Accessed: Jul. 15, 2018.
@article{3226-18,
url = {http://sigport.org/3226},
author = {Fernando Gama; Geert Leus; Antonio Marques; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Convolutional Neural Networks via Node-Varying Graph Filters},
year = {2018} }
TY - EJOUR
T1 - Convolutional Neural Networks via Node-Varying Graph Filters
AU - Fernando Gama; Geert Leus; Antonio Marques; Alejandro Ribeiro
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3226
ER -
Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro. (2018). Convolutional Neural Networks via Node-Varying Graph Filters. IEEE SigPort. http://sigport.org/3226
Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro, 2018. Convolutional Neural Networks via Node-Varying Graph Filters. Available at: http://sigport.org/3226.
Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro. (2018). "Convolutional Neural Networks via Node-Varying Graph Filters." Web.
1. Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro. Convolutional Neural Networks via Node-Varying Graph Filters [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3226

Deep CNN Sparse Coding Analysis


Deep Convolutional Sparse Coding (D-CSC) is a framework reminiscent
of deep convolutional neural nets (DCNN), but by omitting the learning of the
dictionaries one can more transparently analyse the role of the
activation function and its ability to recover activation paths
through the layers. Papyan, Romano, and Elad conducted an analysis of
such an architecture \cite{2016arXiv160708194P}, demonstrated the
relationship with DCNNs and proved conditions under which a D-CSC is
guaranteed to recover activation paths. A technical innovation of

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Authors:
Michael Murray, Jared Tanner
Submitted On:
31 May 2018 - 12:05pm
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Deep CNN Sparse Coding Analysis.pdf

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[1] Michael Murray, Jared Tanner, "Deep CNN Sparse Coding Analysis", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3225. Accessed: Jul. 15, 2018.
@article{3225-18,
url = {http://sigport.org/3225},
author = {Michael Murray; Jared Tanner },
publisher = {IEEE SigPort},
title = {Deep CNN Sparse Coding Analysis},
year = {2018} }
TY - EJOUR
T1 - Deep CNN Sparse Coding Analysis
AU - Michael Murray; Jared Tanner
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3225
ER -
Michael Murray, Jared Tanner. (2018). Deep CNN Sparse Coding Analysis. IEEE SigPort. http://sigport.org/3225
Michael Murray, Jared Tanner, 2018. Deep CNN Sparse Coding Analysis. Available at: http://sigport.org/3225.
Michael Murray, Jared Tanner. (2018). "Deep CNN Sparse Coding Analysis." Web.
1. Michael Murray, Jared Tanner. Deep CNN Sparse Coding Analysis [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3225

MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS


Deep learning on graphs and in particular, graph convolutional neural networks, have recently attracted significant attention in the machine learning community. Many of such

MotifNet.pdf

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Authors:
Federico Monti, Karl Otness, Michael M. Bronstein
Submitted On:
31 May 2018 - 10:30am
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[1] Federico Monti, Karl Otness, Michael M. Bronstein, "MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3224. Accessed: Jul. 15, 2018.
@article{3224-18,
url = {http://sigport.org/3224},
author = {Federico Monti; Karl Otness; Michael M. Bronstein },
publisher = {IEEE SigPort},
title = {MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS},
year = {2018} }
TY - EJOUR
T1 - MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS
AU - Federico Monti; Karl Otness; Michael M. Bronstein
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3224
ER -
Federico Monti, Karl Otness, Michael M. Bronstein. (2018). MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS. IEEE SigPort. http://sigport.org/3224
Federico Monti, Karl Otness, Michael M. Bronstein, 2018. MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS. Available at: http://sigport.org/3224.
Federico Monti, Karl Otness, Michael M. Bronstein. (2018). "MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS." Web.
1. Federico Monti, Karl Otness, Michael M. Bronstein. MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3224

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
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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. 15, 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

COMPUTATIONAL STRATEGIES FOR STATISTICAL INFERENCE BASED ON EMPIRICAL OPTIMAL TRANSPORT


In this talk we discuss some recent limit laws for empirical optimal transport distances from a simulation perspective. On discrete spaces, this requires to solve another optimal transport problem in each simulation step, which reveals simulations of such limit laws computational demanding. We discuss an approximation strategy to overcome this burden. In particular, we examine empirically an upper bound for such limiting distributions on discrete spaces based on a spanning tree approximation which can be computed explicitly.

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Authors:
Carla Tameling, Axel Munk
Submitted On:
31 May 2018 - 2:52am
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Presentation Slides Learning Lecture

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[1] Carla Tameling, Axel Munk, "COMPUTATIONAL STRATEGIES FOR STATISTICAL INFERENCE BASED ON EMPIRICAL OPTIMAL TRANSPORT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3222. Accessed: Jul. 15, 2018.
@article{3222-18,
url = {http://sigport.org/3222},
author = {Carla Tameling; Axel Munk },
publisher = {IEEE SigPort},
title = {COMPUTATIONAL STRATEGIES FOR STATISTICAL INFERENCE BASED ON EMPIRICAL OPTIMAL TRANSPORT},
year = {2018} }
TY - EJOUR
T1 - COMPUTATIONAL STRATEGIES FOR STATISTICAL INFERENCE BASED ON EMPIRICAL OPTIMAL TRANSPORT
AU - Carla Tameling; Axel Munk
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3222
ER -
Carla Tameling, Axel Munk. (2018). COMPUTATIONAL STRATEGIES FOR STATISTICAL INFERENCE BASED ON EMPIRICAL OPTIMAL TRANSPORT. IEEE SigPort. http://sigport.org/3222
Carla Tameling, Axel Munk, 2018. COMPUTATIONAL STRATEGIES FOR STATISTICAL INFERENCE BASED ON EMPIRICAL OPTIMAL TRANSPORT. Available at: http://sigport.org/3222.
Carla Tameling, Axel Munk. (2018). "COMPUTATIONAL STRATEGIES FOR STATISTICAL INFERENCE BASED ON EMPIRICAL OPTIMAL TRANSPORT." Web.
1. Carla Tameling, Axel Munk. COMPUTATIONAL STRATEGIES FOR STATISTICAL INFERENCE BASED ON EMPIRICAL OPTIMAL TRANSPORT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3222

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