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Machine Learning for Signal Processing

Deep Signal Recovery With One-Bit Quantization

Shahin Khobahi
Mojtaba Soltanalian

Paper Details

Authors:
Shahin Khobahi
Mojtaba Soltanalian
Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar
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10 May 2019 - 6:46pm
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[1] Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar, "Deep Signal Recovery With One-Bit Quantization", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4422. Accessed: Oct. 16, 2019.
@article{4422-19,
url = {http://sigport.org/4422},
author = {Shahin Khobahi; Naveed Naimipour; Mojtaba Soltanalian; Yonina C. Eldar },
publisher = {IEEE SigPort},
title = {Deep Signal Recovery With One-Bit Quantization},
year = {2019} }
TY - EJOUR
T1 - Deep Signal Recovery With One-Bit Quantization
AU - Shahin Khobahi; Naveed Naimipour; Mojtaba Soltanalian; Yonina C. Eldar
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4422
ER -
Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar. (2019). Deep Signal Recovery With One-Bit Quantization. IEEE SigPort. http://sigport.org/4422
Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar, 2019. Deep Signal Recovery With One-Bit Quantization. Available at: http://sigport.org/4422.
Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar. (2019). "Deep Signal Recovery With One-Bit Quantization." Web.
1. Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar. Deep Signal Recovery With One-Bit Quantization [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4422

Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm

Deqing Wang

Tensor decomposition is a powerful tool for analyzing multiway data. Nowadays, with the fast development of multisensor technology, more and more data appear in higher-order (order >= 4) and nonnegative form. However, the decomposition of higher-order nonnegative tensor suffers from poor convergence and low speed. In this study, we propose a new nonnegative CANDECOM/PARAFAC (NCP) model using proximal algorithm. The block principal pivoting method in alternating nonnegative least squares (ANLS) framework is employed to minimize the objective function.

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Authors:
Deqing Wang
Deqing Wang, Fengyu Cong, Tapani Ristaniemi
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10 May 2019 - 5:09pm
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[1] Deqing Wang, Fengyu Cong, Tapani Ristaniemi, "Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4408. Accessed: Oct. 16, 2019.
@article{4408-19,
url = {http://sigport.org/4408},
author = {Deqing Wang; Fengyu Cong; Tapani Ristaniemi },
publisher = {IEEE SigPort},
title = {Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm},
year = {2019} }
TY - EJOUR
T1 - Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm
AU - Deqing Wang; Fengyu Cong; Tapani Ristaniemi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4408
ER -
Deqing Wang, Fengyu Cong, Tapani Ristaniemi. (2019). Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm. IEEE SigPort. http://sigport.org/4408
Deqing Wang, Fengyu Cong, Tapani Ristaniemi, 2019. Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm. Available at: http://sigport.org/4408.
Deqing Wang, Fengyu Cong, Tapani Ristaniemi. (2019). "Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm." Web.
1. Deqing Wang, Fengyu Cong, Tapani Ristaniemi. Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4408

DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES

Or Yair

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Authors:
Or Yair
Or Yair, Danny Eytan, Ronen Talmon
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10 May 2019 - 2:37pm
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[1] Or Yair, Danny Eytan, Ronen Talmon, " DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4389. Accessed: Oct. 16, 2019.
@article{4389-19,
url = {http://sigport.org/4389},
author = {Or Yair; Danny Eytan; Ronen Talmon },
publisher = {IEEE SigPort},
title = { DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES},
year = {2019} }
TY - EJOUR
T1 - DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES
AU - Or Yair; Danny Eytan; Ronen Talmon
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4389
ER -
Or Yair, Danny Eytan, Ronen Talmon. (2019). DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES. IEEE SigPort. http://sigport.org/4389
Or Yair, Danny Eytan, Ronen Talmon, 2019. DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES. Available at: http://sigport.org/4389.
Or Yair, Danny Eytan, Ronen Talmon. (2019). " DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES." Web.
1. Or Yair, Danny Eytan, Ronen Talmon. DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4389

DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES

Or Yair

icassp19poster.pdf

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Authors:
Or Yair
Gal Maman, Or Yair, Danny Eytan, Ronen Talmon
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10 May 2019 - 2:37pm
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[1] Gal Maman, Or Yair, Danny Eytan, Ronen Talmon, " DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4387. Accessed: Oct. 16, 2019.
@article{4387-19,
url = {http://sigport.org/4387},
author = {Gal Maman; Or Yair; Danny Eytan; Ronen Talmon },
publisher = {IEEE SigPort},
title = { DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES},
year = {2019} }
TY - EJOUR
T1 - DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES
AU - Gal Maman; Or Yair; Danny Eytan; Ronen Talmon
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4387
ER -
Gal Maman, Or Yair, Danny Eytan, Ronen Talmon. (2019). DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES. IEEE SigPort. http://sigport.org/4387
Gal Maman, Or Yair, Danny Eytan, Ronen Talmon, 2019. DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES. Available at: http://sigport.org/4387.
Gal Maman, Or Yair, Danny Eytan, Ronen Talmon. (2019). " DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES." Web.
1. Gal Maman, Or Yair, Danny Eytan, Ronen Talmon. DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4387

Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning

Yue Jin

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Authors:
Yue Jin
Submitted On:
10 May 2019 - 1:06pm
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[1] , "Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4374. Accessed: Oct. 16, 2019.
@article{4374-19,
url = {http://sigport.org/4374},
author = { },
publisher = {IEEE SigPort},
title = {Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning},
year = {2019} }
TY - EJOUR
T1 - Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4374
ER -
. (2019). Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning. IEEE SigPort. http://sigport.org/4374
, 2019. Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning. Available at: http://sigport.org/4374.
. (2019). "Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning." Web.
1. . Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4374

Prediction of multi-target dynamics using discrete descriptors: An interactive approach

Damian Campo

We propose a probabilistic method to track and interpret interactions of moving objects. The proposed method is based on the analysis of location data from different moving objects that modify their dynamics according to rules of interactions, namely attractive and repulsive forces governing moving objects in a scene. Our method uses a Bayesian structure to identify key elements of the interplay rules and facilitates the prediction of objects' dynamics as the interacting system.

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Authors:
Damian Campo
M. Baydoun, D. Campo, D. Kanapram, L. Marcenaro, C. Regazzoni
Submitted On:
10 May 2019 - 12:18pm
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[1] M. Baydoun, D. Campo, D. Kanapram, L. Marcenaro, C. Regazzoni , "Prediction of multi-target dynamics using discrete descriptors: An interactive approach", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4369. Accessed: Oct. 16, 2019.
@article{4369-19,
url = {http://sigport.org/4369},
author = {M. Baydoun; D. Campo; D. Kanapram; L. Marcenaro; C. Regazzoni },
publisher = {IEEE SigPort},
title = {Prediction of multi-target dynamics using discrete descriptors: An interactive approach},
year = {2019} }
TY - EJOUR
T1 - Prediction of multi-target dynamics using discrete descriptors: An interactive approach
AU - M. Baydoun; D. Campo; D. Kanapram; L. Marcenaro; C. Regazzoni
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4369
ER -
M. Baydoun, D. Campo, D. Kanapram, L. Marcenaro, C. Regazzoni . (2019). Prediction of multi-target dynamics using discrete descriptors: An interactive approach. IEEE SigPort. http://sigport.org/4369
M. Baydoun, D. Campo, D. Kanapram, L. Marcenaro, C. Regazzoni , 2019. Prediction of multi-target dynamics using discrete descriptors: An interactive approach. Available at: http://sigport.org/4369.
M. Baydoun, D. Campo, D. Kanapram, L. Marcenaro, C. Regazzoni . (2019). "Prediction of multi-target dynamics using discrete descriptors: An interactive approach." Web.
1. M. Baydoun, D. Campo, D. Kanapram, L. Marcenaro, C. Regazzoni . Prediction of multi-target dynamics using discrete descriptors: An interactive approach [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4369

COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING

Konstantinos Makantasis
Athanasios Voulodimos

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Authors:
Konstantinos Makantasis
Athanasios Voulodimos
Konstantinos Makantasis, , Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos
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10 May 2019 - 10:54am
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[1] Konstantinos Makantasis, , Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos, "COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4352. Accessed: Oct. 16, 2019.
@article{4352-19,
url = {http://sigport.org/4352},
author = {Konstantinos Makantasis; ; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos },
publisher = {IEEE SigPort},
title = {COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING},
year = {2019} }
TY - EJOUR
T1 - COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING
AU - Konstantinos Makantasis; ; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4352
ER -
Konstantinos Makantasis, , Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos. (2019). COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING. IEEE SigPort. http://sigport.org/4352
Konstantinos Makantasis, , Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos, 2019. COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING. Available at: http://sigport.org/4352.
Konstantinos Makantasis, , Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos. (2019). "COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING." Web.
1. Konstantinos Makantasis, , Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos. COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4352

JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA

Haimiao Zhang

CT image reconstruction from incomplete data, such as sparse views and limited angle reconstruction, is an important and challenging problem in medical imaging. This work proposes a new deep convolutional neural network (CNN), called JSR-Net, that jointly reconstructs CT images and their associated Radon domain projections. JSR-Net combines the traditional model-based approach with deep architecture design of deep learning. A hybrid loss function is adapted to improve the performance of the JSR-Net making it more effective in protecting important image structures.

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Authors:
Haimiao Zhang
Haimiao Zhang, Bin Dong, Baodong Liu
Submitted On:
10 May 2019 - 3:25am
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[1] Haimiao Zhang, Bin Dong, Baodong Liu, "JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4281. Accessed: Oct. 16, 2019.
@article{4281-19,
url = {http://sigport.org/4281},
author = {Haimiao Zhang; Bin Dong; Baodong Liu },
publisher = {IEEE SigPort},
title = {JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA},
year = {2019} }
TY - EJOUR
T1 - JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA
AU - Haimiao Zhang; Bin Dong; Baodong Liu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4281
ER -
Haimiao Zhang, Bin Dong, Baodong Liu. (2019). JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA. IEEE SigPort. http://sigport.org/4281
Haimiao Zhang, Bin Dong, Baodong Liu, 2019. JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA. Available at: http://sigport.org/4281.
Haimiao Zhang, Bin Dong, Baodong Liu. (2019). "JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA." Web.
1. Haimiao Zhang, Bin Dong, Baodong Liu. JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4281

Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis

Louis Jensen

It is well known that electromagnetic and power side-channel attacks allow extraction of unintended information from a computer processor. However, little work has been done to quantify how small a sample is needed in order to glean meaningful information about a program's execution. This paper quantifies this minimum context by training a deep-learning model to track and classify program block types given small windows of side-channel data. We show that a window containing approximately four clock cycles suffices to predict block type with our experimental setup.

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Authors:
Louis Jensen
Louis Jensen, Gavin Brown, Xiao Wang, Jacob Harer, Sang Chin
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9 May 2019 - 8:19am
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Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis

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[1] Louis Jensen, Gavin Brown, Xiao Wang, Jacob Harer, Sang Chin, "Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4196. Accessed: Oct. 16, 2019.
@article{4196-19,
url = {http://sigport.org/4196},
author = {Louis Jensen; Gavin Brown; Xiao Wang; Jacob Harer; Sang Chin },
publisher = {IEEE SigPort},
title = {Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis},
year = {2019} }
TY - EJOUR
T1 - Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis
AU - Louis Jensen; Gavin Brown; Xiao Wang; Jacob Harer; Sang Chin
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4196
ER -
Louis Jensen, Gavin Brown, Xiao Wang, Jacob Harer, Sang Chin. (2019). Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis. IEEE SigPort. http://sigport.org/4196
Louis Jensen, Gavin Brown, Xiao Wang, Jacob Harer, Sang Chin, 2019. Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis. Available at: http://sigport.org/4196.
Louis Jensen, Gavin Brown, Xiao Wang, Jacob Harer, Sang Chin. (2019). "Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis." Web.
1. Louis Jensen, Gavin Brown, Xiao Wang, Jacob Harer, Sang Chin. Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4196

A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface

Joshua Giles
Lyudmila Mihaylova

One of the major limitations of current electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the long calibration time. Due to a high level of noise and non-stationarity inherent in EEG signals, a calibration model trained using the limited number of train data may not yield an accurate BCI model. To address this problem, this paper proposes a novel subject-to-subject transfer learning framework that improves the classification accuracy using limited training data.

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Authors:
Joshua Giles
Lyudmila Mihaylova
Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh
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9 May 2019 - 7:04am
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[1] Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh, "A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4188. Accessed: Oct. 16, 2019.
@article{4188-19,
url = {http://sigport.org/4188},
author = {Joshua Giles; Kai Keng Ang; Lyudmila S. Mihaylova; Mahnaz Arvaneh },
publisher = {IEEE SigPort},
title = {A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface},
year = {2019} }
TY - EJOUR
T1 - A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface
AU - Joshua Giles; Kai Keng Ang; Lyudmila S. Mihaylova; Mahnaz Arvaneh
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4188
ER -
Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh. (2019). A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface. IEEE SigPort. http://sigport.org/4188
Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh, 2019. A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface. Available at: http://sigport.org/4188.
Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh. (2019). "A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface." Web.
1. Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh. A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4188

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