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MLSP 2019

The 29th MLSP workshop in the series of workshops organized by the IEEE Signal Processing Society MLSP Technical Committee will take place at the University of Pittsburgh Campus, Pittsburgh, PA, USA and present the most recent and exciting advances in machine learning for signal processing through keynote talks, tutorials, special and regular single-track sessions as well as matchmaking events.

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
Submitted On:
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. 15, 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

End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks


In this contribution, we introduce convolutional neural network architectures aiming at performing end-to-end detection of attacks to voice biometrics systems, i.e. the model provides scores corresponding to the likelihood of attack given general purpose time-frequency features obtained from speech. Microphone level attackers based on speech synthesis and voice conversion techniques are considered, along with presentation replay attacks.

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Authors:
Joao Monteiro,Jahangir Alam,Tiago H. Falk
Submitted On:
6 November 2019 - 2:12pm
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MLSP_E2E_SpoofingDetection.pdf

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[1] Joao Monteiro,Jahangir Alam,Tiago H. Falk, "End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4916. Accessed: Nov. 15, 2019.
@article{4916-19,
url = {http://sigport.org/4916},
author = {Joao Monteiro;Jahangir Alam;Tiago H. Falk },
publisher = {IEEE SigPort},
title = {End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks},
year = {2019} }
TY - EJOUR
T1 - End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks
AU - Joao Monteiro;Jahangir Alam;Tiago H. Falk
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4916
ER -
Joao Monteiro,Jahangir Alam,Tiago H. Falk. (2019). End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks. IEEE SigPort. http://sigport.org/4916
Joao Monteiro,Jahangir Alam,Tiago H. Falk, 2019. End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks. Available at: http://sigport.org/4916.
Joao Monteiro,Jahangir Alam,Tiago H. Falk. (2019). "End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks." Web.
1. Joao Monteiro,Jahangir Alam,Tiago H. Falk. End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4916

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
Submitted On:
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. 15, 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

Efficient Capon-based approach exploiting temporal windowing for electric network frequency estimation


Electric Network Frequency (ENF) fluctuations constitute a powerful tool in multimedia forensics. An efficient approach for ENF estimation is introduced with temporal windowing based on the filter-bank Capon spectral estimator. A type of Gohberg-Semencul factorization of the model covariance matrix is used due to the Toeplitz structure of the covariance matrix. Moreover, this approach uses, for the first time in the field of ENF, a temporal window, not necessarily the rectangular one, at the stage preceding spectral estimation.

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Authors:
George Karantaidis,Constantine Kotropoulos
Submitted On:
4 November 2019 - 4:04am
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poster2019.pdf

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[1] George Karantaidis,Constantine Kotropoulos, "Efficient Capon-based approach exploiting temporal windowing for electric network frequency estimation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4905. Accessed: Nov. 15, 2019.
@article{4905-19,
url = {http://sigport.org/4905},
author = {George Karantaidis;Constantine Kotropoulos },
publisher = {IEEE SigPort},
title = {Efficient Capon-based approach exploiting temporal windowing for electric network frequency estimation},
year = {2019} }
TY - EJOUR
T1 - Efficient Capon-based approach exploiting temporal windowing for electric network frequency estimation
AU - George Karantaidis;Constantine Kotropoulos
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4905
ER -
George Karantaidis,Constantine Kotropoulos. (2019). Efficient Capon-based approach exploiting temporal windowing for electric network frequency estimation. IEEE SigPort. http://sigport.org/4905
George Karantaidis,Constantine Kotropoulos, 2019. Efficient Capon-based approach exploiting temporal windowing for electric network frequency estimation. Available at: http://sigport.org/4905.
George Karantaidis,Constantine Kotropoulos. (2019). "Efficient Capon-based approach exploiting temporal windowing for electric network frequency estimation." Web.
1. George Karantaidis,Constantine Kotropoulos. Efficient Capon-based approach exploiting temporal windowing for electric network frequency estimation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4905

Insights into the behaviour of multi-task deep neural networks for medical image segmentation


Glandular morphology is used by pathologists to assess the malignancy of different adenocarcinomas. This process involves conducting gland segmentation task. The common approach in specialised domains, such as medical imaging, is to design complex architectures in a multi-task learning setup. Generally, these approaches rely on substantial postprocessing efforts. Moreover, a predominant notion is that general purpose models are not suitable for gland instance segmentation. We analyse the behaviour of two architectures: SA-FCN and Mask R-CNN.

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Authors:
Juanjo R. Guillamon, Line H. Nielsen
Submitted On:
1 November 2019 - 11:34am
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Insigths_into_the_behaviour_of_multi_task_deep_neural_networks_for_medical_image_segmentation.pdf

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[1] Juanjo R. Guillamon, Line H. Nielsen, "Insights into the behaviour of multi-task deep neural networks for medical image segmentation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4902. Accessed: Nov. 15, 2019.
@article{4902-19,
url = {http://sigport.org/4902},
author = {Juanjo R. Guillamon; Line H. Nielsen },
publisher = {IEEE SigPort},
title = {Insights into the behaviour of multi-task deep neural networks for medical image segmentation},
year = {2019} }
TY - EJOUR
T1 - Insights into the behaviour of multi-task deep neural networks for medical image segmentation
AU - Juanjo R. Guillamon; Line H. Nielsen
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4902
ER -
Juanjo R. Guillamon, Line H. Nielsen. (2019). Insights into the behaviour of multi-task deep neural networks for medical image segmentation. IEEE SigPort. http://sigport.org/4902
Juanjo R. Guillamon, Line H. Nielsen, 2019. Insights into the behaviour of multi-task deep neural networks for medical image segmentation. Available at: http://sigport.org/4902.
Juanjo R. Guillamon, Line H. Nielsen. (2019). "Insights into the behaviour of multi-task deep neural networks for medical image segmentation." Web.
1. Juanjo R. Guillamon, Line H. Nielsen. Insights into the behaviour of multi-task deep neural networks for medical image segmentation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4902

A deep network for single-snapshot direction of arrival estimation


This paper examines a deep feedforward network for beamforming with the single--snapshot Sample Covariance Matrix (SCM). The Conventional beamforming formulation, typically quadratic in the complex weight space, is reformulated as real and linear in the weight covariance and SCM. The reformulated SCMs are used as input to a deep feed--forward neural network (FNN) for two source localization. Simulations demonstrate the effect of source incoherence and performance in a noisy tracking example.

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Authors:
Peter Gerstoft, Emma Ozanich, Haiqiang Niu
Submitted On:
28 October 2019 - 10:56am
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conference_poster_6.pdf

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[1] Peter Gerstoft, Emma Ozanich, Haiqiang Niu, "A deep network for single-snapshot direction of arrival estimation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4898. Accessed: Nov. 15, 2019.
@article{4898-19,
url = {http://sigport.org/4898},
author = {Peter Gerstoft; Emma Ozanich; Haiqiang Niu },
publisher = {IEEE SigPort},
title = {A deep network for single-snapshot direction of arrival estimation},
year = {2019} }
TY - EJOUR
T1 - A deep network for single-snapshot direction of arrival estimation
AU - Peter Gerstoft; Emma Ozanich; Haiqiang Niu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4898
ER -
Peter Gerstoft, Emma Ozanich, Haiqiang Niu. (2019). A deep network for single-snapshot direction of arrival estimation. IEEE SigPort. http://sigport.org/4898
Peter Gerstoft, Emma Ozanich, Haiqiang Niu, 2019. A deep network for single-snapshot direction of arrival estimation. Available at: http://sigport.org/4898.
Peter Gerstoft, Emma Ozanich, Haiqiang Niu. (2019). "A deep network for single-snapshot direction of arrival estimation." Web.
1. Peter Gerstoft, Emma Ozanich, Haiqiang Niu. A deep network for single-snapshot direction of arrival estimation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4898

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
Submitted On:
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. 15, 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

Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis

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Authors:
Sai K. Popuri, Zois Boukouvalas
Submitted On:
25 October 2019 - 4:30pm
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[1] Sai K. Popuri, Zois Boukouvalas, "Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4896. Accessed: Nov. 15, 2019.
@article{4896-19,
url = {http://sigport.org/4896},
author = {Sai K. Popuri; Zois Boukouvalas },
publisher = {IEEE SigPort},
title = {Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis},
year = {2019} }
TY - EJOUR
T1 - Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis
AU - Sai K. Popuri; Zois Boukouvalas
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4896
ER -
Sai K. Popuri, Zois Boukouvalas. (2019). Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis. IEEE SigPort. http://sigport.org/4896
Sai K. Popuri, Zois Boukouvalas, 2019. Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis. Available at: http://sigport.org/4896.
Sai K. Popuri, Zois Boukouvalas. (2019). "Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis." Web.
1. Sai K. Popuri, Zois Boukouvalas. Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4896

Wave Physics Informed Dictionary Learning in One Dimension

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Authors:
Harsha Vardhan Tetali, K. Supreet Alguri, Joel B. Harley
Submitted On:
25 October 2019 - 1:50pm
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[1] Harsha Vardhan Tetali, K. Supreet Alguri, Joel B. Harley, "Wave Physics Informed Dictionary Learning in One Dimension", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4895. Accessed: Nov. 15, 2019.
@article{4895-19,
url = {http://sigport.org/4895},
author = {Harsha Vardhan Tetali; K. Supreet Alguri; Joel B. Harley },
publisher = {IEEE SigPort},
title = {Wave Physics Informed Dictionary Learning in One Dimension},
year = {2019} }
TY - EJOUR
T1 - Wave Physics Informed Dictionary Learning in One Dimension
AU - Harsha Vardhan Tetali; K. Supreet Alguri; Joel B. Harley
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4895
ER -
Harsha Vardhan Tetali, K. Supreet Alguri, Joel B. Harley. (2019). Wave Physics Informed Dictionary Learning in One Dimension. IEEE SigPort. http://sigport.org/4895
Harsha Vardhan Tetali, K. Supreet Alguri, Joel B. Harley, 2019. Wave Physics Informed Dictionary Learning in One Dimension. Available at: http://sigport.org/4895.
Harsha Vardhan Tetali, K. Supreet Alguri, Joel B. Harley. (2019). "Wave Physics Informed Dictionary Learning in One Dimension." Web.
1. Harsha Vardhan Tetali, K. Supreet Alguri, Joel B. Harley. Wave Physics Informed Dictionary Learning in One Dimension [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4895

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
Submitted On:
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. 15, 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

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