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Other applications of machine learning (MLR-APPL)

Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver


Super-Resolution (SR) is a technique that has been exhaustively exploited and incorporates strategic aspects to image processing. As quantum computers gradually evolve and provide unconditional proof of computational advantage at solving intractable problems over their classical counterparts, quantum computing emerges with the compelling prospect to offer exponential speedup to process computationally expensive operations, such as the ones verified in SR imaging.

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14 November 2019 - 4:13pm
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GlobalSIP Presentation (Ystallonne Alves).pdf

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[1] , "Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4956. Accessed: Nov. 21, 2019.
@article{4956-19,
url = {http://sigport.org/4956},
author = { },
publisher = {IEEE SigPort},
title = {Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver},
year = {2019} }
TY - EJOUR
T1 - Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4956
ER -
. (2019). Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver. IEEE SigPort. http://sigport.org/4956
, 2019. Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver. Available at: http://sigport.org/4956.
. (2019). "Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver." Web.
1. . Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4956

GMM-UBM based Person Verification using footfall signatures for Smart Home Applications


In this paper, we propose a novel person verification system based on footfall signatures using Gaussian Mixture Model-Universal Background Model (GMM-UBM). Ground vibration generated by footfall of an individual is used as a biometric modality. We conduct extensive experiments to compare the proposed technique with various baselines of footfall based person verification. The system is evaluated on an indigenous dataset containing 7750 footfall events of twenty subjects.

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Authors:
Bodhibrata Mukhpadhay, Manohar Parvatini, Subrat Kar
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7 November 2019 - 8:39am
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[1] Bodhibrata Mukhpadhay, Manohar Parvatini, Subrat Kar, "GMM-UBM based Person Verification using footfall signatures for Smart Home Applications", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4923. Accessed: Nov. 21, 2019.
@article{4923-19,
url = {http://sigport.org/4923},
author = {Bodhibrata Mukhpadhay; Manohar Parvatini; Subrat Kar },
publisher = {IEEE SigPort},
title = {GMM-UBM based Person Verification using footfall signatures for Smart Home Applications},
year = {2019} }
TY - EJOUR
T1 - GMM-UBM based Person Verification using footfall signatures for Smart Home Applications
AU - Bodhibrata Mukhpadhay; Manohar Parvatini; Subrat Kar
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4923
ER -
Bodhibrata Mukhpadhay, Manohar Parvatini, Subrat Kar. (2019). GMM-UBM based Person Verification using footfall signatures for Smart Home Applications. IEEE SigPort. http://sigport.org/4923
Bodhibrata Mukhpadhay, Manohar Parvatini, Subrat Kar, 2019. GMM-UBM based Person Verification using footfall signatures for Smart Home Applications. Available at: http://sigport.org/4923.
Bodhibrata Mukhpadhay, Manohar Parvatini, Subrat Kar. (2019). "GMM-UBM based Person Verification using footfall signatures for Smart Home Applications." Web.
1. Bodhibrata Mukhpadhay, Manohar Parvatini, Subrat Kar. GMM-UBM based Person Verification using footfall signatures for Smart Home Applications [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4923

A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI


The clinical analysis of magnetic resonance (MR) can be accelerated through the undersampling in the k-space (Fourier domain). Deep learning techniques have been recently received considerable interest for accelerating MR imaging (MRI). In this paper, a deep learning method for accelerating MRI is presented, which is able to reconstruct undersampled MR images obtained by reducing the k-space data in the direction of the phase encoding.

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Authors:
Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini
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7 November 2019 - 5:38am
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Poster

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[1] Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini, "A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4919. Accessed: Nov. 21, 2019.
@article{4919-19,
url = {http://sigport.org/4919},
author = {Antonio Falvo; Danilo Comminiello; Simone Scardapane; Michele Scarpiniti; Aurelio Uncini },
publisher = {IEEE SigPort},
title = {A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI},
year = {2019} }
TY - EJOUR
T1 - A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI
AU - Antonio Falvo; Danilo Comminiello; Simone Scardapane; Michele Scarpiniti; Aurelio Uncini
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4919
ER -
Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini. (2019). A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI. IEEE SigPort. http://sigport.org/4919
Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini, 2019. A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI. Available at: http://sigport.org/4919.
Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini. (2019). "A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI." Web.
1. Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini. A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4919

A WEIGHTED ORDERED PROBIT COLLABORATIVE KALMAN FILTER FOR HOTEL RATING PREDICTION


A successful recommender system interacts with users and learns their preferences. This is crucial in order to provide accurate recommendations. In this paper, a Weighted Ordered Probit Collaborative Kalman filter is proposed for hotel rating prediction. Since potential changes may occur in hotel services or accommodation conditions, a hotel popularity may be volatile through time. A weighted ordered probit model is introduced to capture this latent trend about each hotel popularity through time.

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Authors:
Myrsini Demi, Constantine Kotropoulos, Emmanouil Gionanidis
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4 November 2019 - 10:42am
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A WEIGHTED ORDERED PROBIT COLLABORATIVE KALMAN FILTER

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[1] Myrsini Demi, Constantine Kotropoulos, Emmanouil Gionanidis, "A WEIGHTED ORDERED PROBIT COLLABORATIVE KALMAN FILTER FOR HOTEL RATING PREDICTION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4908. Accessed: Nov. 21, 2019.
@article{4908-19,
url = {http://sigport.org/4908},
author = {Myrsini Demi; Constantine Kotropoulos; Emmanouil Gionanidis },
publisher = {IEEE SigPort},
title = {A WEIGHTED ORDERED PROBIT COLLABORATIVE KALMAN FILTER FOR HOTEL RATING PREDICTION},
year = {2019} }
TY - EJOUR
T1 - A WEIGHTED ORDERED PROBIT COLLABORATIVE KALMAN FILTER FOR HOTEL RATING PREDICTION
AU - Myrsini Demi; Constantine Kotropoulos; Emmanouil Gionanidis
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4908
ER -
Myrsini Demi, Constantine Kotropoulos, Emmanouil Gionanidis. (2019). A WEIGHTED ORDERED PROBIT COLLABORATIVE KALMAN FILTER FOR HOTEL RATING PREDICTION. IEEE SigPort. http://sigport.org/4908
Myrsini Demi, Constantine Kotropoulos, Emmanouil Gionanidis, 2019. A WEIGHTED ORDERED PROBIT COLLABORATIVE KALMAN FILTER FOR HOTEL RATING PREDICTION. Available at: http://sigport.org/4908.
Myrsini Demi, Constantine Kotropoulos, Emmanouil Gionanidis. (2019). "A WEIGHTED ORDERED PROBIT COLLABORATIVE KALMAN FILTER FOR HOTEL RATING PREDICTION." Web.
1. Myrsini Demi, Constantine Kotropoulos, Emmanouil Gionanidis. A WEIGHTED ORDERED PROBIT COLLABORATIVE KALMAN FILTER FOR HOTEL RATING PREDICTION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4908

Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting


The growing edge computing paradigm, notably the vision of the internet-of-things (IoT), calls for a new epitome of lightweight algorithms. Currently, the most successful models that learn from temporal data, which is prevalent in IoT applications, stem from the field of deep learning. However, these models evince extended training times and heavy resource requirements, prohibiting training in constrained environments. To address these concerns, we employ deep stochastic neural networks from the reservoir computing paradigm.

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Authors:
Zachariah Carmichael, Dhireesha Kudithipudi
Submitted On:
29 October 2019 - 2:12pm
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Presentation Slides

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[1] Zachariah Carmichael, Dhireesha Kudithipudi, "Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4899. Accessed: Nov. 21, 2019.
@article{4899-19,
url = {http://sigport.org/4899},
author = {Zachariah Carmichael; Dhireesha Kudithipudi },
publisher = {IEEE SigPort},
title = {Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting},
year = {2019} }
TY - EJOUR
T1 - Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting
AU - Zachariah Carmichael; Dhireesha Kudithipudi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4899
ER -
Zachariah Carmichael, Dhireesha Kudithipudi. (2019). Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting. IEEE SigPort. http://sigport.org/4899
Zachariah Carmichael, Dhireesha Kudithipudi, 2019. Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting. Available at: http://sigport.org/4899.
Zachariah Carmichael, Dhireesha Kudithipudi. (2019). "Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting." Web.
1. Zachariah Carmichael, Dhireesha Kudithipudi. Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4899

On Convergence of Projected Gradient Descent for Minimizing a Large Scale Quadratic over the Unit Sphere


Unit sphere-constrained quadratic optimization has been studied extensively over the past decades. While state-of-art algorithms for solving this problem often rely on relaxation or approximation techniques, there has been little research into scalable first-order methods that tackle the problem in its original form. These first-order methods are often more well-suited for the big data setting. In this paper, we provide a novel analysis of the simple projected gradient descent method for minimizing a quadratic over a sphere.

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Authors:
Trung Vu, Raviv Raich, Xiao Fu
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26 October 2019 - 2:40pm
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MLSP2019.pdf

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[1] Trung Vu, Raviv Raich, Xiao Fu, "On Convergence of Projected Gradient Descent for Minimizing a Large Scale Quadratic over the Unit Sphere", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4897. Accessed: Nov. 21, 2019.
@article{4897-19,
url = {http://sigport.org/4897},
author = {Trung Vu; Raviv Raich; Xiao Fu },
publisher = {IEEE SigPort},
title = {On Convergence of Projected Gradient Descent for Minimizing a Large Scale Quadratic over the Unit Sphere},
year = {2019} }
TY - EJOUR
T1 - On Convergence of Projected Gradient Descent for Minimizing a Large Scale Quadratic over the Unit Sphere
AU - Trung Vu; Raviv Raich; Xiao Fu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4897
ER -
Trung Vu, Raviv Raich, Xiao Fu. (2019). On Convergence of Projected Gradient Descent for Minimizing a Large Scale Quadratic over the Unit Sphere. IEEE SigPort. http://sigport.org/4897
Trung Vu, Raviv Raich, Xiao Fu, 2019. On Convergence of Projected Gradient Descent for Minimizing a Large Scale Quadratic over the Unit Sphere. Available at: http://sigport.org/4897.
Trung Vu, Raviv Raich, Xiao Fu. (2019). "On Convergence of Projected Gradient Descent for Minimizing a Large Scale Quadratic over the Unit Sphere." Web.
1. Trung Vu, Raviv Raich, Xiao Fu. On Convergence of Projected Gradient Descent for Minimizing a Large Scale Quadratic over the Unit Sphere [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4897

Interpretable Online Banking Fraud Detection based on Hierarchical Attention Mechanism


Online banking activities are constantly growing and are likely to become even more common as digital banking platforms evolve. One side effect of this trend is the rise in attempted fraud. However, there is very little work in the literature on online banking fraud detection. We propose an attention based architecture for classifying online banking transactions as either fraudulent or genuine. The proposed method allows transparency to its decision by identifying the most important transactions in the sequence and the most informative features in each transaction.

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Authors:
Sarit Kraus, Jacob Goldberger
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25 October 2019 - 9:32am
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Interpretable Online Banking Fraud Detection Based on Hierarchical Attention Mechanism-poster.pdf

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[1] Sarit Kraus, Jacob Goldberger, "Interpretable Online Banking Fraud Detection based on Hierarchical Attention Mechanism", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4894. Accessed: Nov. 21, 2019.
@article{4894-19,
url = {http://sigport.org/4894},
author = {Sarit Kraus; Jacob Goldberger },
publisher = {IEEE SigPort},
title = {Interpretable Online Banking Fraud Detection based on Hierarchical Attention Mechanism},
year = {2019} }
TY - EJOUR
T1 - Interpretable Online Banking Fraud Detection based on Hierarchical Attention Mechanism
AU - Sarit Kraus; Jacob Goldberger
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4894
ER -
Sarit Kraus, Jacob Goldberger. (2019). Interpretable Online Banking Fraud Detection based on Hierarchical Attention Mechanism. IEEE SigPort. http://sigport.org/4894
Sarit Kraus, Jacob Goldberger, 2019. Interpretable Online Banking Fraud Detection based on Hierarchical Attention Mechanism. Available at: http://sigport.org/4894.
Sarit Kraus, Jacob Goldberger. (2019). "Interpretable Online Banking Fraud Detection based on Hierarchical Attention Mechanism." Web.
1. Sarit Kraus, Jacob Goldberger. Interpretable Online Banking Fraud Detection based on Hierarchical Attention Mechanism [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4894

Single-Pixel Camera Sensing Matrix Design For Hierarchical Compressed Spectral Clustering


Compressive spectral imaging (CSI) acquires random projections of a spectral scene. Typically, before applying any post-processing task, e.g. clustering, it is required a computationally expensive reconstruction of the underlying 3D scene. Therefore, several works focus on improving the reconstruction quality by adaptively designing the sensing matrix aiming at better post-processing results. Instead, this paper proposes a hierarchical adaptive approach to design a sensing matrix of the single-pixel camera, such that pixel clustering can be performed in the compressed domain.

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24 October 2019 - 9:53am
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SPC Design for Hierarchical Compressed Spectral Clustering

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[1] , "Single-Pixel Camera Sensing Matrix Design For Hierarchical Compressed Spectral Clustering", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4887. Accessed: Nov. 21, 2019.
@article{4887-19,
url = {http://sigport.org/4887},
author = { },
publisher = {IEEE SigPort},
title = {Single-Pixel Camera Sensing Matrix Design For Hierarchical Compressed Spectral Clustering},
year = {2019} }
TY - EJOUR
T1 - Single-Pixel Camera Sensing Matrix Design For Hierarchical Compressed Spectral Clustering
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4887
ER -
. (2019). Single-Pixel Camera Sensing Matrix Design For Hierarchical Compressed Spectral Clustering. IEEE SigPort. http://sigport.org/4887
, 2019. Single-Pixel Camera Sensing Matrix Design For Hierarchical Compressed Spectral Clustering. Available at: http://sigport.org/4887.
. (2019). "Single-Pixel Camera Sensing Matrix Design For Hierarchical Compressed Spectral Clustering." Web.
1. . Single-Pixel Camera Sensing Matrix Design For Hierarchical Compressed Spectral Clustering [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4887

Optimal Mobile Relay Beamforming via Reinforcement Learning

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Authors:
Konstantinos Diamantaras, Athina Petropulu
Submitted On:
24 October 2019 - 5:20am
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[1] Konstantinos Diamantaras, Athina Petropulu, "Optimal Mobile Relay Beamforming via Reinforcement Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4885. Accessed: Nov. 21, 2019.
@article{4885-19,
url = {http://sigport.org/4885},
author = {Konstantinos Diamantaras; Athina Petropulu },
publisher = {IEEE SigPort},
title = {Optimal Mobile Relay Beamforming via Reinforcement Learning},
year = {2019} }
TY - EJOUR
T1 - Optimal Mobile Relay Beamforming via Reinforcement Learning
AU - Konstantinos Diamantaras; Athina Petropulu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4885
ER -
Konstantinos Diamantaras, Athina Petropulu. (2019). Optimal Mobile Relay Beamforming via Reinforcement Learning. IEEE SigPort. http://sigport.org/4885
Konstantinos Diamantaras, Athina Petropulu, 2019. Optimal Mobile Relay Beamforming via Reinforcement Learning. Available at: http://sigport.org/4885.
Konstantinos Diamantaras, Athina Petropulu. (2019). "Optimal Mobile Relay Beamforming via Reinforcement Learning." Web.
1. Konstantinos Diamantaras, Athina Petropulu. Optimal Mobile Relay Beamforming via Reinforcement Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4885

Self-supervised representation learning from electroencephalography signals


The supervised learning paradigm is limited by the cost - and sometimes the impracticality - of data collection and labeling in multiple domains. Self-supervised learning, a paradigm which exploits the structure of unlabeled data to create learning problems that can be solved with standard supervised approaches, has shown great promise as a pretraining or feature learning approach in fields like computer vision and time series processing. In this work, we present self-supervision strategies that can be used to learn informative representations from multivariate time series.

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Authors:
Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, Alexandre Gramfort
Submitted On:
13 October 2019 - 8:58pm
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mlsp2019_poster_hjb_final.pdf

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[1] Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, Alexandre Gramfort, "Self-supervised representation learning from electroencephalography signals", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4866. Accessed: Nov. 21, 2019.
@article{4866-19,
url = {http://sigport.org/4866},
author = {Hubert Banville; Isabela Albuquerque; Aapo Hyvärinen; Graeme Moffat; Denis-Alexander Engemann; Alexandre Gramfort },
publisher = {IEEE SigPort},
title = {Self-supervised representation learning from electroencephalography signals},
year = {2019} }
TY - EJOUR
T1 - Self-supervised representation learning from electroencephalography signals
AU - Hubert Banville; Isabela Albuquerque; Aapo Hyvärinen; Graeme Moffat; Denis-Alexander Engemann; Alexandre Gramfort
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4866
ER -
Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, Alexandre Gramfort. (2019). Self-supervised representation learning from electroencephalography signals. IEEE SigPort. http://sigport.org/4866
Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, Alexandre Gramfort, 2019. Self-supervised representation learning from electroencephalography signals. Available at: http://sigport.org/4866.
Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, Alexandre Gramfort. (2019). "Self-supervised representation learning from electroencephalography signals." Web.
1. Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, Alexandre Gramfort. Self-supervised representation learning from electroencephalography signals [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4866

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