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Other applications of machine learning (MLR-APPL)

On Deep Learning-based Massive MIMO Indoor User Localization


We examine the usability of deep neural networks for multiple-input multiple-output (MIMO) user positioning solely based on the orthogonal frequency division multiplex (OFDM) complex channel coefficients. In contrast to other indoor positioning systems (IPSs), the proposed method does not require any additional piloting overhead or any other changes in the communications system itself as it is deployed on top of an existing OFDM MIMO system. Supported by actual measurements, we are mainly interested in the more challenging non-line of sight (NLoS) scenario.

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Authors:
Maximilian Arnold, Stephan ten Brink
Submitted On:
21 June 2018 - 11:57am
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spawc_positioning_poster.pdf

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[1] Maximilian Arnold, Stephan ten Brink, "On Deep Learning-based Massive MIMO Indoor User Localization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3286. Accessed: Jun. 22, 2018.
@article{3286-18,
url = {http://sigport.org/3286},
author = {Maximilian Arnold; Stephan ten Brink },
publisher = {IEEE SigPort},
title = {On Deep Learning-based Massive MIMO Indoor User Localization},
year = {2018} }
TY - EJOUR
T1 - On Deep Learning-based Massive MIMO Indoor User Localization
AU - Maximilian Arnold; Stephan ten Brink
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3286
ER -
Maximilian Arnold, Stephan ten Brink. (2018). On Deep Learning-based Massive MIMO Indoor User Localization. IEEE SigPort. http://sigport.org/3286
Maximilian Arnold, Stephan ten Brink, 2018. On Deep Learning-based Massive MIMO Indoor User Localization. Available at: http://sigport.org/3286.
Maximilian Arnold, Stephan ten Brink. (2018). "On Deep Learning-based Massive MIMO Indoor User Localization." Web.
1. Maximilian Arnold, Stephan ten Brink. On Deep Learning-based Massive MIMO Indoor User Localization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3286

Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks


Full-duplex systems require very strong self-interference cancellation in order to operate correctly and a significant part of the self-interference signal is due to non-linear effects created by various transceiver impairments. As such, linear cancellation alone is usually not sufficient and sophisticated non-linear cancellation algorithms have been proposed in the literature. In this work, we investigate the use of a neural network as an alternative to the traditional non-linear cancellation method that is based on polynomial basis functions.

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21 June 2018 - 7:51am
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18SPAWCPoster.pdf

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[1] , "Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3283. Accessed: Jun. 22, 2018.
@article{3283-18,
url = {http://sigport.org/3283},
author = { },
publisher = {IEEE SigPort},
title = {Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks},
year = {2018} }
TY - EJOUR
T1 - Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3283
ER -
. (2018). Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks. IEEE SigPort. http://sigport.org/3283
, 2018. Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks. Available at: http://sigport.org/3283.
. (2018). "Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks." Web.
1. . Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3283

Self-paced mixture of t distribution model


Gaussian mixture model (GMM) is a powerful probabilistic model for representing the probability distribution of observations in the population. However, the fitness of Gaussian mixture model can be significantly degraded when the data contain a certain amount of outliers. Although there are certain variants of GMM (e.g., mixture of Laplace, mixture of t distribution) attempting to handle outliers, none of them can sufficiently mitigate the effect of outliers if the outliers are far from the centroids.

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Authors:
Qingtao Tang, Li Niu, Tao Dai, Xi Xiao, Shu-Tao Xia
Submitted On:
27 May 2018 - 10:23pm
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icassp-landscape.pdf

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[1] Qingtao Tang, Li Niu, Tao Dai, Xi Xiao, Shu-Tao Xia, "Self-paced mixture of t distribution model", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3210. Accessed: Jun. 22, 2018.
@article{3210-18,
url = {http://sigport.org/3210},
author = {Qingtao Tang; Li Niu; Tao Dai; Xi Xiao; Shu-Tao Xia },
publisher = {IEEE SigPort},
title = {Self-paced mixture of t distribution model},
year = {2018} }
TY - EJOUR
T1 - Self-paced mixture of t distribution model
AU - Qingtao Tang; Li Niu; Tao Dai; Xi Xiao; Shu-Tao Xia
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3210
ER -
Qingtao Tang, Li Niu, Tao Dai, Xi Xiao, Shu-Tao Xia. (2018). Self-paced mixture of t distribution model. IEEE SigPort. http://sigport.org/3210
Qingtao Tang, Li Niu, Tao Dai, Xi Xiao, Shu-Tao Xia, 2018. Self-paced mixture of t distribution model. Available at: http://sigport.org/3210.
Qingtao Tang, Li Niu, Tao Dai, Xi Xiao, Shu-Tao Xia. (2018). "Self-paced mixture of t distribution model." Web.
1. Qingtao Tang, Li Niu, Tao Dai, Xi Xiao, Shu-Tao Xia. Self-paced mixture of t distribution model [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3210

Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks


Head movement is an integral part of face-to-face communications. It is important to investigate methodologies to generate naturalistic movements for conversational agents (CAs). The predominant method for head movement generation is using rules based on the meaning of the message. However, the variations of head movements by these methods are bounded by the predefined dictionary of gestures. Speech-driven methods offer an alternative approach, learning the relationship between speech and head movements from real recordings.

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Authors:
Najmeh Sadoughi, Carlos Busso
Submitted On:
1 May 2018 - 8:43pm
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Sadoughi_2018-poster.pdf

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[1] Najmeh Sadoughi, Carlos Busso, "Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3198. Accessed: Jun. 22, 2018.
@article{3198-18,
url = {http://sigport.org/3198},
author = {Najmeh Sadoughi; Carlos Busso },
publisher = {IEEE SigPort},
title = {Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks},
year = {2018} }
TY - EJOUR
T1 - Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks
AU - Najmeh Sadoughi; Carlos Busso
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3198
ER -
Najmeh Sadoughi, Carlos Busso. (2018). Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks. IEEE SigPort. http://sigport.org/3198
Najmeh Sadoughi, Carlos Busso, 2018. Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks. Available at: http://sigport.org/3198.
Najmeh Sadoughi, Carlos Busso. (2018). "Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks." Web.
1. Najmeh Sadoughi, Carlos Busso. Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3198

Deep learning for predicting image memorability


Memorability of media content such as images and videos has recently become an important research subject in computer vision. This paper presents our computation model for predicting image memorability, which is based on a deep learning architecture designed for a classification task. We exploit the use of both convolutional neural network (CNN) - based visual features and semantic features related to image captioning for the task. We train and test our model on the large-scale benchmarking memorability dataset: LaMem.

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Authors:
Hammad Squalli-Houssaini, Ngoc Q. K. Duong, Marquant Gwenaelle and Claire-Helene Demarty
Submitted On:
25 April 2018 - 4:30am
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Presentation_final.pdf

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[1] Hammad Squalli-Houssaini, Ngoc Q. K. Duong, Marquant Gwenaelle and Claire-Helene Demarty, "Deep learning for predicting image memorability", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3176. Accessed: Jun. 22, 2018.
@article{3176-18,
url = {http://sigport.org/3176},
author = {Hammad Squalli-Houssaini; Ngoc Q. K. Duong; Marquant Gwenaelle and Claire-Helene Demarty },
publisher = {IEEE SigPort},
title = {Deep learning for predicting image memorability},
year = {2018} }
TY - EJOUR
T1 - Deep learning for predicting image memorability
AU - Hammad Squalli-Houssaini; Ngoc Q. K. Duong; Marquant Gwenaelle and Claire-Helene Demarty
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3176
ER -
Hammad Squalli-Houssaini, Ngoc Q. K. Duong, Marquant Gwenaelle and Claire-Helene Demarty. (2018). Deep learning for predicting image memorability. IEEE SigPort. http://sigport.org/3176
Hammad Squalli-Houssaini, Ngoc Q. K. Duong, Marquant Gwenaelle and Claire-Helene Demarty, 2018. Deep learning for predicting image memorability. Available at: http://sigport.org/3176.
Hammad Squalli-Houssaini, Ngoc Q. K. Duong, Marquant Gwenaelle and Claire-Helene Demarty. (2018). "Deep learning for predicting image memorability." Web.
1. Hammad Squalli-Houssaini, Ngoc Q. K. Duong, Marquant Gwenaelle and Claire-Helene Demarty. Deep learning for predicting image memorability [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3176

Using Block Coordinate Descent to Learn Sparse Coding Dictionaries with a Matrix Norm Update


Researchers have recently examined a modified approach to sparse coding that encourages dictionaries to learn anomalous features. This is done by incorporating the matrix 1-norm, or \ell_{1,\infty} mixed matrix norm, into the dictionary update portion of a sparse coding algorithm. However, solving a matrix norm minimization problem in each iteration of the algorithm

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Authors:
Bradley M Whitaker, David V Anderson
Submitted On:
23 April 2018 - 1:16pm
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Whitaker_ICASSP_Poster

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[1] Bradley M Whitaker, David V Anderson, "Using Block Coordinate Descent to Learn Sparse Coding Dictionaries with a Matrix Norm Update", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3152. Accessed: Jun. 22, 2018.
@article{3152-18,
url = {http://sigport.org/3152},
author = {Bradley M Whitaker; David V Anderson },
publisher = {IEEE SigPort},
title = {Using Block Coordinate Descent to Learn Sparse Coding Dictionaries with a Matrix Norm Update},
year = {2018} }
TY - EJOUR
T1 - Using Block Coordinate Descent to Learn Sparse Coding Dictionaries with a Matrix Norm Update
AU - Bradley M Whitaker; David V Anderson
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3152
ER -
Bradley M Whitaker, David V Anderson. (2018). Using Block Coordinate Descent to Learn Sparse Coding Dictionaries with a Matrix Norm Update. IEEE SigPort. http://sigport.org/3152
Bradley M Whitaker, David V Anderson, 2018. Using Block Coordinate Descent to Learn Sparse Coding Dictionaries with a Matrix Norm Update. Available at: http://sigport.org/3152.
Bradley M Whitaker, David V Anderson. (2018). "Using Block Coordinate Descent to Learn Sparse Coding Dictionaries with a Matrix Norm Update." Web.
1. Bradley M Whitaker, David V Anderson. Using Block Coordinate Descent to Learn Sparse Coding Dictionaries with a Matrix Norm Update [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3152

COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION


Deep Neural Network (DNN) is a basic method used for the rare Acoustic Event Detection (AED) in synthesised audio. The structure of DNNs including Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN) for AED tasks has rather fewer hidden layers compared with computer vision systems. This paper tries to demonstrate that a DNN with more hidden layers does not necessarily guarantee a better performance in AED tasks.

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Authors:
Shengchen Li
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19 April 2018 - 9:58pm
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COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION.pdf

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[1] Shengchen Li, "COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3053. Accessed: Jun. 22, 2018.
@article{3053-18,
url = {http://sigport.org/3053},
author = {Shengchen Li },
publisher = {IEEE SigPort},
title = {COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION},
year = {2018} }
TY - EJOUR
T1 - COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION
AU - Shengchen Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3053
ER -
Shengchen Li. (2018). COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION. IEEE SigPort. http://sigport.org/3053
Shengchen Li, 2018. COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION. Available at: http://sigport.org/3053.
Shengchen Li. (2018). "COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION." Web.
1. Shengchen Li. COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3053

The Landscape of Non-convex Quadratic Feasibility


Motivated by applications such as ordinal embedding and collaborative ranking, we formulate homogeneous quadratic feasibility as an unconstrained, non-convex minimization problem. Our work aims to understand the landscape (local minimizers and global minimizers) of the non-convex objective, which corresponds to hinge losses arising from quadratic constraints. Under certain assumptions, we give necessary conditions for non-global, local minimizers of our objective and additionally show that in two dimensions, every local minimizer is a global minimizer.

ICASSP_v4.pdf

PDF icon ICASSP_v4.pdf (32 downloads)

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Authors:
Lalit Jain, Laura Balzano
Submitted On:
19 April 2018 - 2:10pm
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ICASSP_v4.pdf

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[1] Lalit Jain, Laura Balzano, "The Landscape of Non-convex Quadratic Feasibility", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2798. Accessed: Jun. 22, 2018.
@article{2798-18,
url = {http://sigport.org/2798},
author = {Lalit Jain; Laura Balzano },
publisher = {IEEE SigPort},
title = {The Landscape of Non-convex Quadratic Feasibility},
year = {2018} }
TY - EJOUR
T1 - The Landscape of Non-convex Quadratic Feasibility
AU - Lalit Jain; Laura Balzano
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2798
ER -
Lalit Jain, Laura Balzano. (2018). The Landscape of Non-convex Quadratic Feasibility. IEEE SigPort. http://sigport.org/2798
Lalit Jain, Laura Balzano, 2018. The Landscape of Non-convex Quadratic Feasibility. Available at: http://sigport.org/2798.
Lalit Jain, Laura Balzano. (2018). "The Landscape of Non-convex Quadratic Feasibility." Web.
1. Lalit Jain, Laura Balzano. The Landscape of Non-convex Quadratic Feasibility [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2798

RFCM for Data Association and Multitarget Tracking Using 3D Radar


erformance of object classification using 3D automotive radar relies on accurate data association and multitarget tracking, which are greatly affected by data bias and proximity of objects to each other. A regularized fuzzy c-means (RFCM) algorithm is proposed herein to resolve the data association uncertainty problem that has shown to outperform the conventional FCM algorithm. The proposed method exploits results from the companion tracker to increase performance robustness. Simulation results using simulated and field data have proven the efficacy of the proposed method.

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Authors:
Chun-Nien Chan, Carrson C. Fung
Submitted On:
13 April 2018 - 11:39am
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ICASSP 2018 poster

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[1] Chun-Nien Chan, Carrson C. Fung, "RFCM for Data Association and Multitarget Tracking Using 3D Radar", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2721. Accessed: Jun. 22, 2018.
@article{2721-18,
url = {http://sigport.org/2721},
author = {Chun-Nien Chan; Carrson C. Fung },
publisher = {IEEE SigPort},
title = {RFCM for Data Association and Multitarget Tracking Using 3D Radar},
year = {2018} }
TY - EJOUR
T1 - RFCM for Data Association and Multitarget Tracking Using 3D Radar
AU - Chun-Nien Chan; Carrson C. Fung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2721
ER -
Chun-Nien Chan, Carrson C. Fung. (2018). RFCM for Data Association and Multitarget Tracking Using 3D Radar. IEEE SigPort. http://sigport.org/2721
Chun-Nien Chan, Carrson C. Fung, 2018. RFCM for Data Association and Multitarget Tracking Using 3D Radar. Available at: http://sigport.org/2721.
Chun-Nien Chan, Carrson C. Fung. (2018). "RFCM for Data Association and Multitarget Tracking Using 3D Radar." Web.
1. Chun-Nien Chan, Carrson C. Fung. RFCM for Data Association and Multitarget Tracking Using 3D Radar [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2721

EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY


Dynamic functional connectivity has become a prominent approach for tracking the changes of macroscale statistical dependencies between regions in the brain. Effective parametrization of these statistical dependencies, referred to as brain states, is however still an open problem. We investigate different emission models in the hidden Markov model framework, each representing certain assumptions about dynamic changes in the brain.

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Authors:
Søren Føns Vind Nielsen, Yuri Levin-Schwartz, Diego Vidaurre, Tulay Adali, Vince D. Calhoun, Kristoffer H. Madsen, Lars Kai Hansen, Morten Mørup
Submitted On:
13 April 2018 - 10:02am
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posterICASSP2018.pdf

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[1] Søren Føns Vind Nielsen, Yuri Levin-Schwartz, Diego Vidaurre, Tulay Adali, Vince D. Calhoun, Kristoffer H. Madsen, Lars Kai Hansen, Morten Mørup, "EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2707. Accessed: Jun. 22, 2018.
@article{2707-18,
url = {http://sigport.org/2707},
author = {Søren Føns Vind Nielsen; Yuri Levin-Schwartz; Diego Vidaurre; Tulay Adali; Vince D. Calhoun; Kristoffer H. Madsen; Lars Kai Hansen; Morten Mørup },
publisher = {IEEE SigPort},
title = {EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY},
year = {2018} }
TY - EJOUR
T1 - EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY
AU - Søren Føns Vind Nielsen; Yuri Levin-Schwartz; Diego Vidaurre; Tulay Adali; Vince D. Calhoun; Kristoffer H. Madsen; Lars Kai Hansen; Morten Mørup
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2707
ER -
Søren Føns Vind Nielsen, Yuri Levin-Schwartz, Diego Vidaurre, Tulay Adali, Vince D. Calhoun, Kristoffer H. Madsen, Lars Kai Hansen, Morten Mørup. (2018). EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY. IEEE SigPort. http://sigport.org/2707
Søren Føns Vind Nielsen, Yuri Levin-Schwartz, Diego Vidaurre, Tulay Adali, Vince D. Calhoun, Kristoffer H. Madsen, Lars Kai Hansen, Morten Mørup, 2018. EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY. Available at: http://sigport.org/2707.
Søren Føns Vind Nielsen, Yuri Levin-Schwartz, Diego Vidaurre, Tulay Adali, Vince D. Calhoun, Kristoffer H. Madsen, Lars Kai Hansen, Morten Mørup. (2018). "EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY." Web.
1. Søren Føns Vind Nielsen, Yuri Levin-Schwartz, Diego Vidaurre, Tulay Adali, Vince D. Calhoun, Kristoffer H. Madsen, Lars Kai Hansen, Morten Mørup. EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2707

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