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Neural network learning (MLR-NNLR)

SEQUENCE SEGMENTATION USING JOINT RNN AND STRUCTURED PREDICTION MODELS

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Authors:
Yossi adi, Joseph Keshet, Emily Cibelli, Matthew Goldrick
Submitted On:
28 February 2017 - 7:54am
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[1] Yossi adi, Joseph Keshet, Emily Cibelli, Matthew Goldrick, "SEQUENCE SEGMENTATION USING JOINT RNN AND STRUCTURED PREDICTION MODELS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1509. Accessed: Aug. 19, 2018.
@article{1509-17,
url = {http://sigport.org/1509},
author = {Yossi adi; Joseph Keshet; Emily Cibelli; Matthew Goldrick },
publisher = {IEEE SigPort},
title = {SEQUENCE SEGMENTATION USING JOINT RNN AND STRUCTURED PREDICTION MODELS},
year = {2017} }
TY - EJOUR
T1 - SEQUENCE SEGMENTATION USING JOINT RNN AND STRUCTURED PREDICTION MODELS
AU - Yossi adi; Joseph Keshet; Emily Cibelli; Matthew Goldrick
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1509
ER -
Yossi adi, Joseph Keshet, Emily Cibelli, Matthew Goldrick. (2017). SEQUENCE SEGMENTATION USING JOINT RNN AND STRUCTURED PREDICTION MODELS. IEEE SigPort. http://sigport.org/1509
Yossi adi, Joseph Keshet, Emily Cibelli, Matthew Goldrick, 2017. SEQUENCE SEGMENTATION USING JOINT RNN AND STRUCTURED PREDICTION MODELS. Available at: http://sigport.org/1509.
Yossi adi, Joseph Keshet, Emily Cibelli, Matthew Goldrick. (2017). "SEQUENCE SEGMENTATION USING JOINT RNN AND STRUCTURED PREDICTION MODELS." Web.
1. Yossi adi, Joseph Keshet, Emily Cibelli, Matthew Goldrick. SEQUENCE SEGMENTATION USING JOINT RNN AND STRUCTURED PREDICTION MODELS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1509

MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION


Recurrent neural networks (RNNs) have shown clear superiority in sequence modeling, particularly the ones with gated units, such as long short-term memory (LSTM) and gated recurrent unit (GRU). However, the dynamic properties behind the remarkable performance remain unclear in many applications, e.g., automatic speech recognition (ASR). This paper employs visualization techniques to study the behavior of LSTM and GRU when performing speech recognition tasks.

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Authors:
Zhiyuan Tang, Ying Shi, Dong Wang, Yang Feng, Shiyue Zhang
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4 March 2017 - 3:11am
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[1] Zhiyuan Tang, Ying Shi, Dong Wang, Yang Feng, Shiyue Zhang, "MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1463. Accessed: Aug. 19, 2018.
@article{1463-17,
url = {http://sigport.org/1463},
author = {Zhiyuan Tang; Ying Shi; Dong Wang; Yang Feng; Shiyue Zhang },
publisher = {IEEE SigPort},
title = {MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION},
year = {2017} }
TY - EJOUR
T1 - MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION
AU - Zhiyuan Tang; Ying Shi; Dong Wang; Yang Feng; Shiyue Zhang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1463
ER -
Zhiyuan Tang, Ying Shi, Dong Wang, Yang Feng, Shiyue Zhang. (2017). MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION. IEEE SigPort. http://sigport.org/1463
Zhiyuan Tang, Ying Shi, Dong Wang, Yang Feng, Shiyue Zhang, 2017. MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION. Available at: http://sigport.org/1463.
Zhiyuan Tang, Ying Shi, Dong Wang, Yang Feng, Shiyue Zhang. (2017). "MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION." Web.
1. Zhiyuan Tang, Ying Shi, Dong Wang, Yang Feng, Shiyue Zhang. MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1463

Onsager-corrected deep learning for sparse linear inverse problems


Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem encountered in compressive sensing, where one seeks to recover a sparse signal from a few noisy linear measurements. In this paper, we propose two novel neural-network architectures that decouple prediction errors across layers in the same way that the approximate message passing (AMP) algorithms decouple them across iterations: through Onsager correction.

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Authors:
Mark Borgerding and Philip Schniter
Submitted On:
6 December 2016 - 10:30am
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[1] Mark Borgerding and Philip Schniter, "Onsager-corrected deep learning for sparse linear inverse problems", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1359. Accessed: Aug. 19, 2018.
@article{1359-16,
url = {http://sigport.org/1359},
author = {Mark Borgerding and Philip Schniter },
publisher = {IEEE SigPort},
title = {Onsager-corrected deep learning for sparse linear inverse problems},
year = {2016} }
TY - EJOUR
T1 - Onsager-corrected deep learning for sparse linear inverse problems
AU - Mark Borgerding and Philip Schniter
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1359
ER -
Mark Borgerding and Philip Schniter. (2016). Onsager-corrected deep learning for sparse linear inverse problems. IEEE SigPort. http://sigport.org/1359
Mark Borgerding and Philip Schniter, 2016. Onsager-corrected deep learning for sparse linear inverse problems. Available at: http://sigport.org/1359.
Mark Borgerding and Philip Schniter. (2016). "Onsager-corrected deep learning for sparse linear inverse problems." Web.
1. Mark Borgerding and Philip Schniter. Onsager-corrected deep learning for sparse linear inverse problems [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1359

COMPLEX INPUT CONVOLUTIONAL NEURAL NETWORKS FOR WIDE ANGLE SAR ATR


To date, automatic target recognition (ATR) techniques in synthetic aperture radar (SAR) imagery have largely focused on features that use only the magnitude part of SAR’s complex valued magnitude-plus-phase history. While such techniques are often very successful, they inherently ignore the significant amount of discriminatory information available in the phase. This paper describes a method for exploiting the complex information for ATR by using a convolutional neural network (CNN) that accepts fully complex input features.

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Authors:
Michael Wilmanski, Chris Kreucher, Alfred Hero
Submitted On:
11 December 2016 - 2:48pm
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[1] Michael Wilmanski, Chris Kreucher, Alfred Hero, "COMPLEX INPUT CONVOLUTIONAL NEURAL NETWORKS FOR WIDE ANGLE SAR ATR", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1351. Accessed: Aug. 19, 2018.
@article{1351-16,
url = {http://sigport.org/1351},
author = {Michael Wilmanski; Chris Kreucher; Alfred Hero },
publisher = {IEEE SigPort},
title = {COMPLEX INPUT CONVOLUTIONAL NEURAL NETWORKS FOR WIDE ANGLE SAR ATR},
year = {2016} }
TY - EJOUR
T1 - COMPLEX INPUT CONVOLUTIONAL NEURAL NETWORKS FOR WIDE ANGLE SAR ATR
AU - Michael Wilmanski; Chris Kreucher; Alfred Hero
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1351
ER -
Michael Wilmanski, Chris Kreucher, Alfred Hero. (2016). COMPLEX INPUT CONVOLUTIONAL NEURAL NETWORKS FOR WIDE ANGLE SAR ATR. IEEE SigPort. http://sigport.org/1351
Michael Wilmanski, Chris Kreucher, Alfred Hero, 2016. COMPLEX INPUT CONVOLUTIONAL NEURAL NETWORKS FOR WIDE ANGLE SAR ATR. Available at: http://sigport.org/1351.
Michael Wilmanski, Chris Kreucher, Alfred Hero. (2016). "COMPLEX INPUT CONVOLUTIONAL NEURAL NETWORKS FOR WIDE ANGLE SAR ATR." Web.
1. Michael Wilmanski, Chris Kreucher, Alfred Hero. COMPLEX INPUT CONVOLUTIONAL NEURAL NETWORKS FOR WIDE ANGLE SAR ATR [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1351

Multi-Task Joint-Learning for Robust Voice Activity Detection


Model based VAD approaches have been widely used and
achieved success in practice. These approaches usually cast
VAD as a frame-level classification problem and employ statistical
classifiers, such as Gaussian Mixture Model (GMM) or
Deep Neural Network (DNN) to assign a speech/silence label
for each frame. Due to the frame independent assumption classification,
the VAD results tend to be fragile. To address this
problem, in this paper, a new structured multi-frame prediction
DNN approach is proposed to improve the segment-level

Paper Details

Authors:
Yanmin Qian, Kai Yu
Submitted On:
15 October 2016 - 3:51am
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[1] Yanmin Qian, Kai Yu, "Multi-Task Joint-Learning for Robust Voice Activity Detection", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1220. Accessed: Aug. 19, 2018.
@article{1220-16,
url = {http://sigport.org/1220},
author = {Yanmin Qian; Kai Yu },
publisher = {IEEE SigPort},
title = {Multi-Task Joint-Learning for Robust Voice Activity Detection},
year = {2016} }
TY - EJOUR
T1 - Multi-Task Joint-Learning for Robust Voice Activity Detection
AU - Yanmin Qian; Kai Yu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1220
ER -
Yanmin Qian, Kai Yu. (2016). Multi-Task Joint-Learning for Robust Voice Activity Detection. IEEE SigPort. http://sigport.org/1220
Yanmin Qian, Kai Yu, 2016. Multi-Task Joint-Learning for Robust Voice Activity Detection. Available at: http://sigport.org/1220.
Yanmin Qian, Kai Yu. (2016). "Multi-Task Joint-Learning for Robust Voice Activity Detection." Web.
1. Yanmin Qian, Kai Yu. Multi-Task Joint-Learning for Robust Voice Activity Detection [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1220

Lecture ICASSP 2016 Pierre Laffitte


This presentation introduces a Deep Learning model that performs classification of the Audio Scene in the subway environment. The final goal is to detect Screams and Shouts for surveillance purposes. The model is a combination of Deep Belief Network and Deep Neural Network, (generatively pre-trained within the DBN framework and fine-tuned discriminatively within the DNN framework), and is trained on a novel database of pseudo-real signals collected in the Paris metro.

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Authors:
Pierre Laffitte, David Sodoyer, Laurent Girin, Charles Tatkeu
Submitted On:
23 March 2016 - 10:01am
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ICASSP Lecture.pdf

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[1] Pierre Laffitte, David Sodoyer, Laurent Girin, Charles Tatkeu, "Lecture ICASSP 2016 Pierre Laffitte", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/991. Accessed: Aug. 19, 2018.
@article{991-16,
url = {http://sigport.org/991},
author = {Pierre Laffitte; David Sodoyer; Laurent Girin; Charles Tatkeu },
publisher = {IEEE SigPort},
title = {Lecture ICASSP 2016 Pierre Laffitte},
year = {2016} }
TY - EJOUR
T1 - Lecture ICASSP 2016 Pierre Laffitte
AU - Pierre Laffitte; David Sodoyer; Laurent Girin; Charles Tatkeu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/991
ER -
Pierre Laffitte, David Sodoyer, Laurent Girin, Charles Tatkeu. (2016). Lecture ICASSP 2016 Pierre Laffitte. IEEE SigPort. http://sigport.org/991
Pierre Laffitte, David Sodoyer, Laurent Girin, Charles Tatkeu, 2016. Lecture ICASSP 2016 Pierre Laffitte. Available at: http://sigport.org/991.
Pierre Laffitte, David Sodoyer, Laurent Girin, Charles Tatkeu. (2016). "Lecture ICASSP 2016 Pierre Laffitte." Web.
1. Pierre Laffitte, David Sodoyer, Laurent Girin, Charles Tatkeu. Lecture ICASSP 2016 Pierre Laffitte [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/991

Supervised Subspace Learning based on Deep Randomized Networks


In this paper, we propose a supervised subspace learning method that exploits the rich representation power of deep feedforward networks. In order to derive a fast, yet efficient, learning scheme we employ deep randomized neural networks that have been recently shown to provide good compromise between training speed and performance.

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Authors:
Alexandros Iosifidis, Moncef Gabbouj
Submitted On:
22 March 2016 - 3:47am
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[1] Alexandros Iosifidis, Moncef Gabbouj, "Supervised Subspace Learning based on Deep Randomized Networks", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/961. Accessed: Aug. 19, 2018.
@article{961-16,
url = {http://sigport.org/961},
author = {Alexandros Iosifidis; Moncef Gabbouj },
publisher = {IEEE SigPort},
title = {Supervised Subspace Learning based on Deep Randomized Networks},
year = {2016} }
TY - EJOUR
T1 - Supervised Subspace Learning based on Deep Randomized Networks
AU - Alexandros Iosifidis; Moncef Gabbouj
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/961
ER -
Alexandros Iosifidis, Moncef Gabbouj. (2016). Supervised Subspace Learning based on Deep Randomized Networks. IEEE SigPort. http://sigport.org/961
Alexandros Iosifidis, Moncef Gabbouj, 2016. Supervised Subspace Learning based on Deep Randomized Networks. Available at: http://sigport.org/961.
Alexandros Iosifidis, Moncef Gabbouj. (2016). "Supervised Subspace Learning based on Deep Randomized Networks." Web.
1. Alexandros Iosifidis, Moncef Gabbouj. Supervised Subspace Learning based on Deep Randomized Networks [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/961

Mediated Mixture of Experts

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Authors:
Sebastian Agethen, Winston H. Hsu
Submitted On:
19 March 2016 - 10:03pm
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[1] Sebastian Agethen, Winston H. Hsu, "Mediated Mixture of Experts", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/850. Accessed: Aug. 19, 2018.
@article{850-16,
url = {http://sigport.org/850},
author = {Sebastian Agethen; Winston H. Hsu },
publisher = {IEEE SigPort},
title = {Mediated Mixture of Experts},
year = {2016} }
TY - EJOUR
T1 - Mediated Mixture of Experts
AU - Sebastian Agethen; Winston H. Hsu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/850
ER -
Sebastian Agethen, Winston H. Hsu. (2016). Mediated Mixture of Experts. IEEE SigPort. http://sigport.org/850
Sebastian Agethen, Winston H. Hsu, 2016. Mediated Mixture of Experts. Available at: http://sigport.org/850.
Sebastian Agethen, Winston H. Hsu. (2016). "Mediated Mixture of Experts." Web.
1. Sebastian Agethen, Winston H. Hsu. Mediated Mixture of Experts [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/850

Face Alignment by Deep Convolutional Network with Adaptive Learning Rate


Deep convolutional network has been widely used in face recognition while not often used in face alignment. One of the most important reasons of this is the lack of training images annotated with landmarks due to fussy and time-consuming annotation work. To overcome this problem, we propose a novel data augmentation strategy. And we design an innovative training algorithm with adaptive learning rate for two iterative procedures, which helps the network to search an optimal solution.

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Authors:
Zhiwen Shao, Shouhong Ding, Hengliang Zhu, Chengjie Wang, Lizhuang Ma
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19 March 2016 - 6:38am
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[1] Zhiwen Shao, Shouhong Ding, Hengliang Zhu, Chengjie Wang, Lizhuang Ma, "Face Alignment by Deep Convolutional Network with Adaptive Learning Rate", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/799. Accessed: Aug. 19, 2018.
@article{799-16,
url = {http://sigport.org/799},
author = {Zhiwen Shao; Shouhong Ding; Hengliang Zhu; Chengjie Wang; Lizhuang Ma },
publisher = {IEEE SigPort},
title = {Face Alignment by Deep Convolutional Network with Adaptive Learning Rate},
year = {2016} }
TY - EJOUR
T1 - Face Alignment by Deep Convolutional Network with Adaptive Learning Rate
AU - Zhiwen Shao; Shouhong Ding; Hengliang Zhu; Chengjie Wang; Lizhuang Ma
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/799
ER -
Zhiwen Shao, Shouhong Ding, Hengliang Zhu, Chengjie Wang, Lizhuang Ma. (2016). Face Alignment by Deep Convolutional Network with Adaptive Learning Rate. IEEE SigPort. http://sigport.org/799
Zhiwen Shao, Shouhong Ding, Hengliang Zhu, Chengjie Wang, Lizhuang Ma, 2016. Face Alignment by Deep Convolutional Network with Adaptive Learning Rate. Available at: http://sigport.org/799.
Zhiwen Shao, Shouhong Ding, Hengliang Zhu, Chengjie Wang, Lizhuang Ma. (2016). "Face Alignment by Deep Convolutional Network with Adaptive Learning Rate." Web.
1. Zhiwen Shao, Shouhong Ding, Hengliang Zhu, Chengjie Wang, Lizhuang Ma. Face Alignment by Deep Convolutional Network with Adaptive Learning Rate [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/799

Characterization and Classification of Sonar Targets Using Ellipsoid Features


We present a geometry-inspired characterization of
target response for active sonar that exploits similarity between
intra-class features to distinguish between different targets
against environmental objects such as a rock. Key innovation is to
represent feature manifolds as a set of ellipsoids, each of which
geometrically encompasses a unique physical characteristic of
the target’s response. We have demonstrated over experimental
field data that for a given target class, these feature ellipsoids

Paper Details

Authors:
Ananya Sen Gupta, Ivars Kirsteins
Submitted On:
23 February 2016 - 1:44pm
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[1] Ananya Sen Gupta, Ivars Kirsteins, "Characterization and Classification of Sonar Targets Using Ellipsoid Features", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/507. Accessed: Aug. 19, 2018.
@article{507-15,
url = {http://sigport.org/507},
author = {Ananya Sen Gupta; Ivars Kirsteins },
publisher = {IEEE SigPort},
title = {Characterization and Classification of Sonar Targets Using Ellipsoid Features},
year = {2015} }
TY - EJOUR
T1 - Characterization and Classification of Sonar Targets Using Ellipsoid Features
AU - Ananya Sen Gupta; Ivars Kirsteins
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/507
ER -
Ananya Sen Gupta, Ivars Kirsteins. (2015). Characterization and Classification of Sonar Targets Using Ellipsoid Features. IEEE SigPort. http://sigport.org/507
Ananya Sen Gupta, Ivars Kirsteins, 2015. Characterization and Classification of Sonar Targets Using Ellipsoid Features. Available at: http://sigport.org/507.
Ananya Sen Gupta, Ivars Kirsteins. (2015). "Characterization and Classification of Sonar Targets Using Ellipsoid Features." Web.
1. Ananya Sen Gupta, Ivars Kirsteins. Characterization and Classification of Sonar Targets Using Ellipsoid Features [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/507

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