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Neural network learning (MLR-NNLR)

Residual Networks of Residual Networks: Multilevel Residual Networks


A residual-networks family with hundreds or even thousands of layers dominates major image recognition tasks, but building a network by simply stacking residual blocks inevitably limits its optimization ability. This paper proposes a novel residual-network architecture, Residual networks of Residual networks (RoR), to dig the optimization ability of residual networks. RoR substitutes optimizing residual mapping of residual mapping for optimizing original residual mapping.

ICIP 2017.pdf

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Authors:
Ke Zhang, Miao Sun, Tony Han, Xingfang Yuan, Liru Guo, Tao Liu
Submitted On:
12 September 2017 - 10:28pm
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ICIP 2017.pdf

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[1] Ke Zhang, Miao Sun, Tony Han, Xingfang Yuan, Liru Guo, Tao Liu, "Residual Networks of Residual Networks: Multilevel Residual Networks", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1954. Accessed: May. 26, 2018.
@article{1954-17,
url = {http://sigport.org/1954},
author = {Ke Zhang; Miao Sun; Tony Han; Xingfang Yuan; Liru Guo; Tao Liu },
publisher = {IEEE SigPort},
title = {Residual Networks of Residual Networks: Multilevel Residual Networks},
year = {2017} }
TY - EJOUR
T1 - Residual Networks of Residual Networks: Multilevel Residual Networks
AU - Ke Zhang; Miao Sun; Tony Han; Xingfang Yuan; Liru Guo; Tao Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1954
ER -
Ke Zhang, Miao Sun, Tony Han, Xingfang Yuan, Liru Guo, Tao Liu. (2017). Residual Networks of Residual Networks: Multilevel Residual Networks. IEEE SigPort. http://sigport.org/1954
Ke Zhang, Miao Sun, Tony Han, Xingfang Yuan, Liru Guo, Tao Liu, 2017. Residual Networks of Residual Networks: Multilevel Residual Networks. Available at: http://sigport.org/1954.
Ke Zhang, Miao Sun, Tony Han, Xingfang Yuan, Liru Guo, Tao Liu. (2017). "Residual Networks of Residual Networks: Multilevel Residual Networks." Web.
1. Ke Zhang, Miao Sun, Tony Han, Xingfang Yuan, Liru Guo, Tao Liu. Residual Networks of Residual Networks: Multilevel Residual Networks [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1954

Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework


In this paper, we introduce an adaptive unsupervised learning framework, which utilizes natural images to train filter sets. The ap- plicability of these filter sets is demonstrated by evaluating their per- formance in two contrasting applications - image quality assessment and texture retrieval. While assessing image quality, the filters need to capture perceptual differences based on dissimilarities between a reference image and its distorted version. In texture retrieval, the filters need to assess similarity between texture images to retrieve closest matching textures.

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Authors:
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib
Submitted On:
11 September 2017 - 6:47pm
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ICIP2017Poster_UnsupervisedFramework_MohitCan.pdf

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[1] Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib, "Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1921. Accessed: May. 26, 2018.
@article{1921-17,
url = {http://sigport.org/1921},
author = {Mohit Prabhushankar; Dogancan Temel; and Ghassan Alregib },
publisher = {IEEE SigPort},
title = {Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework},
year = {2017} }
TY - EJOUR
T1 - Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework
AU - Mohit Prabhushankar; Dogancan Temel; and Ghassan Alregib
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1921
ER -
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib. (2017). Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework. IEEE SigPort. http://sigport.org/1921
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib, 2017. Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework. Available at: http://sigport.org/1921.
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib. (2017). "Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework." Web.
1. Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib. Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1921

Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework


In this paper, we introduce an adaptive unsupervised learning framework, which utilizes natural images to train filter sets. The ap- plicability of these filter sets is demonstrated by evaluating their per- formance in two contrasting applications - image quality assessment and texture retrieval. While assessing image quality, the filters need to capture perceptual differences based on dissimilarities between a reference image and its distorted version. In texture retrieval, the filters need to assess similarity between texture images to retrieve closest matching textures.

Paper Details

Authors:
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib
Submitted On:
11 September 2017 - 6:47pm
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ICIP2017Poster_UnsupervisedFramework_MohitCan.pdf

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[1] Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib, "Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1920. Accessed: May. 26, 2018.
@article{1920-17,
url = {http://sigport.org/1920},
author = {Mohit Prabhushankar; Dogancan Temel; and Ghassan Alregib },
publisher = {IEEE SigPort},
title = {Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework},
year = {2017} }
TY - EJOUR
T1 - Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework
AU - Mohit Prabhushankar; Dogancan Temel; and Ghassan Alregib
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1920
ER -
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib. (2017). Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework. IEEE SigPort. http://sigport.org/1920
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib, 2017. Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework. Available at: http://sigport.org/1920.
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib. (2017). "Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework." Web.
1. Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib. Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1920

Learning a Cross-Modal Hashing Network for Multimedia Search


In this paper, we propose a cross-modal hashing network (CMHN) method to learn compact binary codes for cross-modality multimedia search. Unlike most existing cross-modal hashing methods which learn a single pair of projections to map each example into a binary vector, we design a deep neural network to learn multiple pairs of hierarchical non-linear transformations, under which the nonlinear characteristics of samples can be well exploited and the modality gap is well reduced.

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Authors:
Venice Erin Liong, Jiwen Lu, Yap-Peng Tan
Submitted On:
11 September 2017 - 5:44am
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icip2017_poster_2555.pdf

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[1] Venice Erin Liong, Jiwen Lu, Yap-Peng Tan, "Learning a Cross-Modal Hashing Network for Multimedia Search", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1901. Accessed: May. 26, 2018.
@article{1901-17,
url = {http://sigport.org/1901},
author = {Venice Erin Liong; Jiwen Lu; Yap-Peng Tan },
publisher = {IEEE SigPort},
title = {Learning a Cross-Modal Hashing Network for Multimedia Search},
year = {2017} }
TY - EJOUR
T1 - Learning a Cross-Modal Hashing Network for Multimedia Search
AU - Venice Erin Liong; Jiwen Lu; Yap-Peng Tan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1901
ER -
Venice Erin Liong, Jiwen Lu, Yap-Peng Tan. (2017). Learning a Cross-Modal Hashing Network for Multimedia Search. IEEE SigPort. http://sigport.org/1901
Venice Erin Liong, Jiwen Lu, Yap-Peng Tan, 2017. Learning a Cross-Modal Hashing Network for Multimedia Search. Available at: http://sigport.org/1901.
Venice Erin Liong, Jiwen Lu, Yap-Peng Tan. (2017). "Learning a Cross-Modal Hashing Network for Multimedia Search." Web.
1. Venice Erin Liong, Jiwen Lu, Yap-Peng Tan. Learning a Cross-Modal Hashing Network for Multimedia Search [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1901

Face Aging with Conditional Generative Adversarial Networks

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Authors:
Moez Baccouche, Jean-Luc Dugelay
Submitted On:
15 September 2017 - 9:03am
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Antipov_ICIP_2017_updated.pdf

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[1] Moez Baccouche, Jean-Luc Dugelay, "Face Aging with Conditional Generative Adversarial Networks", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1818. Accessed: May. 26, 2018.
@article{1818-17,
url = {http://sigport.org/1818},
author = {Moez Baccouche; Jean-Luc Dugelay },
publisher = {IEEE SigPort},
title = {Face Aging with Conditional Generative Adversarial Networks},
year = {2017} }
TY - EJOUR
T1 - Face Aging with Conditional Generative Adversarial Networks
AU - Moez Baccouche; Jean-Luc Dugelay
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1818
ER -
Moez Baccouche, Jean-Luc Dugelay. (2017). Face Aging with Conditional Generative Adversarial Networks. IEEE SigPort. http://sigport.org/1818
Moez Baccouche, Jean-Luc Dugelay, 2017. Face Aging with Conditional Generative Adversarial Networks. Available at: http://sigport.org/1818.
Moez Baccouche, Jean-Luc Dugelay. (2017). "Face Aging with Conditional Generative Adversarial Networks." Web.
1. Moez Baccouche, Jean-Luc Dugelay. Face Aging with Conditional Generative Adversarial Networks [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1818

LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (SUPPLEMENTARY MATERIAL)

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Authors:
Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel
Submitted On:
14 September 2017 - 10:45pm
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supplementary

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[1] Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel, "LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (SUPPLEMENTARY MATERIAL)", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1797. Accessed: May. 26, 2018.
@article{1797-17,
url = {http://sigport.org/1797},
author = {Jake Snell; Karl Ridgeway; Renjie Liao; Brett D. Roads; Michael C. Mozer; Richard S. Zemel },
publisher = {IEEE SigPort},
title = {LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (SUPPLEMENTARY MATERIAL)},
year = {2017} }
TY - EJOUR
T1 - LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (SUPPLEMENTARY MATERIAL)
AU - Jake Snell; Karl Ridgeway; Renjie Liao; Brett D. Roads; Michael C. Mozer; Richard S. Zemel
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1797
ER -
Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel. (2017). LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (SUPPLEMENTARY MATERIAL). IEEE SigPort. http://sigport.org/1797
Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel, 2017. LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (SUPPLEMENTARY MATERIAL). Available at: http://sigport.org/1797.
Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel. (2017). "LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (SUPPLEMENTARY MATERIAL)." Web.
1. Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel. LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (SUPPLEMENTARY MATERIAL) [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1797

A Scalable Convolutional Neural Network for Task-specified Scenarios via Knowledge Distillation


In this paper, we explore the redundancy in convolutional neural network, which scales with the complexity of vision tasks. Considering that many front-end visual systems are interested in only a limited range of visual targets, the removing of task-specified network redundancy can promote a wide range of potential applications. We propose a task-specified knowledge distillation algorithm to derive a simplified model with pre-set computation cost and minimized accuracy loss, which suits the resource constraint front-end systems well.

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Authors:
Mengnan Shi, Fei Qin, Qixiang Ye, Zhenjun Han, Jianbin Jiao
Submitted On:
12 March 2017 - 8:20pm
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ICASSP2017 poster 80cm.pdf

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[1] Mengnan Shi, Fei Qin, Qixiang Ye, Zhenjun Han, Jianbin Jiao, "A Scalable Convolutional Neural Network for Task-specified Scenarios via Knowledge Distillation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1751. Accessed: May. 26, 2018.
@article{1751-17,
url = {http://sigport.org/1751},
author = {Mengnan Shi; Fei Qin; Qixiang Ye; Zhenjun Han; Jianbin Jiao },
publisher = {IEEE SigPort},
title = {A Scalable Convolutional Neural Network for Task-specified Scenarios via Knowledge Distillation},
year = {2017} }
TY - EJOUR
T1 - A Scalable Convolutional Neural Network for Task-specified Scenarios via Knowledge Distillation
AU - Mengnan Shi; Fei Qin; Qixiang Ye; Zhenjun Han; Jianbin Jiao
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1751
ER -
Mengnan Shi, Fei Qin, Qixiang Ye, Zhenjun Han, Jianbin Jiao. (2017). A Scalable Convolutional Neural Network for Task-specified Scenarios via Knowledge Distillation. IEEE SigPort. http://sigport.org/1751
Mengnan Shi, Fei Qin, Qixiang Ye, Zhenjun Han, Jianbin Jiao, 2017. A Scalable Convolutional Neural Network for Task-specified Scenarios via Knowledge Distillation. Available at: http://sigport.org/1751.
Mengnan Shi, Fei Qin, Qixiang Ye, Zhenjun Han, Jianbin Jiao. (2017). "A Scalable Convolutional Neural Network for Task-specified Scenarios via Knowledge Distillation." Web.
1. Mengnan Shi, Fei Qin, Qixiang Ye, Zhenjun Han, Jianbin Jiao. A Scalable Convolutional Neural Network for Task-specified Scenarios via Knowledge Distillation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1751

NOISY OBJECTIVE FUNCTIONS BASED ON THE F-DIVERGENCE

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Authors:
Markus Nussbaum-Thom, Ralf Schlueter, Vaibhava Goel, Hermann Ney
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8 March 2017 - 3:58pm
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[1] Markus Nussbaum-Thom, Ralf Schlueter, Vaibhava Goel, Hermann Ney, "NOISY OBJECTIVE FUNCTIONS BASED ON THE F-DIVERGENCE", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1710. Accessed: May. 26, 2018.
@article{1710-17,
url = {http://sigport.org/1710},
author = {Markus Nussbaum-Thom; Ralf Schlueter; Vaibhava Goel; Hermann Ney },
publisher = {IEEE SigPort},
title = {NOISY OBJECTIVE FUNCTIONS BASED ON THE F-DIVERGENCE},
year = {2017} }
TY - EJOUR
T1 - NOISY OBJECTIVE FUNCTIONS BASED ON THE F-DIVERGENCE
AU - Markus Nussbaum-Thom; Ralf Schlueter; Vaibhava Goel; Hermann Ney
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1710
ER -
Markus Nussbaum-Thom, Ralf Schlueter, Vaibhava Goel, Hermann Ney. (2017). NOISY OBJECTIVE FUNCTIONS BASED ON THE F-DIVERGENCE. IEEE SigPort. http://sigport.org/1710
Markus Nussbaum-Thom, Ralf Schlueter, Vaibhava Goel, Hermann Ney, 2017. NOISY OBJECTIVE FUNCTIONS BASED ON THE F-DIVERGENCE. Available at: http://sigport.org/1710.
Markus Nussbaum-Thom, Ralf Schlueter, Vaibhava Goel, Hermann Ney. (2017). "NOISY OBJECTIVE FUNCTIONS BASED ON THE F-DIVERGENCE." Web.
1. Markus Nussbaum-Thom, Ralf Schlueter, Vaibhava Goel, Hermann Ney. NOISY OBJECTIVE FUNCTIONS BASED ON THE F-DIVERGENCE [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1710

Automatic Speech Emotion Recognition Using Recurrent Neural Networks with Local Attention


Automatic emotion recognition from speech is a challenging task which relies heavily on the effectiveness of the speech features used for classification. In this work, we study the use of deep learning to automatically discover emotionally relevant features from speech. It is shown that using a deep recurrent neural network, we can learn both the short-time frame-level acoustic features that are emotionally relevant, as well as an appropriate temporal aggregation of those features into a compact utterance-level representation.

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Authors:
Emad Barsoum, Cha Zhang
Submitted On:
15 March 2017 - 12:33am
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icassp2017.pptx

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[1] Emad Barsoum, Cha Zhang, "Automatic Speech Emotion Recognition Using Recurrent Neural Networks with Local Attention", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1667. Accessed: May. 26, 2018.
@article{1667-17,
url = {http://sigport.org/1667},
author = {Emad Barsoum; Cha Zhang },
publisher = {IEEE SigPort},
title = {Automatic Speech Emotion Recognition Using Recurrent Neural Networks with Local Attention},
year = {2017} }
TY - EJOUR
T1 - Automatic Speech Emotion Recognition Using Recurrent Neural Networks with Local Attention
AU - Emad Barsoum; Cha Zhang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1667
ER -
Emad Barsoum, Cha Zhang. (2017). Automatic Speech Emotion Recognition Using Recurrent Neural Networks with Local Attention. IEEE SigPort. http://sigport.org/1667
Emad Barsoum, Cha Zhang, 2017. Automatic Speech Emotion Recognition Using Recurrent Neural Networks with Local Attention. Available at: http://sigport.org/1667.
Emad Barsoum, Cha Zhang. (2017). "Automatic Speech Emotion Recognition Using Recurrent Neural Networks with Local Attention." Web.
1. Emad Barsoum, Cha Zhang. Automatic Speech Emotion Recognition Using Recurrent Neural Networks with Local Attention [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1667

CHARACTER-LEVEL LANGUAGE MODELING WITH HIERARCHICAL RECURRENT NEURAL NETWORKS


Recurrent neural network (RNN) based character-level language models (CLMs) are extremely useful for modeling out-of-vocabulary words by nature. However, their performance is generally much worse than the word-level language models (WLMs), since CLMs need to consider longer history of tokens to properly predict the next one. We address this problem by proposing hierarchical RNN architectures, which consist of multiple modules with different timescales.

poster.pdf

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Authors:
Kyuyeon Hwang, Wonyong Sung
Submitted On:
6 March 2017 - 3:05am
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[1] Kyuyeon Hwang, Wonyong Sung, "CHARACTER-LEVEL LANGUAGE MODELING WITH HIERARCHICAL RECURRENT NEURAL NETWORKS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1645. Accessed: May. 26, 2018.
@article{1645-17,
url = {http://sigport.org/1645},
author = {Kyuyeon Hwang; Wonyong Sung },
publisher = {IEEE SigPort},
title = {CHARACTER-LEVEL LANGUAGE MODELING WITH HIERARCHICAL RECURRENT NEURAL NETWORKS},
year = {2017} }
TY - EJOUR
T1 - CHARACTER-LEVEL LANGUAGE MODELING WITH HIERARCHICAL RECURRENT NEURAL NETWORKS
AU - Kyuyeon Hwang; Wonyong Sung
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1645
ER -
Kyuyeon Hwang, Wonyong Sung. (2017). CHARACTER-LEVEL LANGUAGE MODELING WITH HIERARCHICAL RECURRENT NEURAL NETWORKS. IEEE SigPort. http://sigport.org/1645
Kyuyeon Hwang, Wonyong Sung, 2017. CHARACTER-LEVEL LANGUAGE MODELING WITH HIERARCHICAL RECURRENT NEURAL NETWORKS. Available at: http://sigport.org/1645.
Kyuyeon Hwang, Wonyong Sung. (2017). "CHARACTER-LEVEL LANGUAGE MODELING WITH HIERARCHICAL RECURRENT NEURAL NETWORKS." Web.
1. Kyuyeon Hwang, Wonyong Sung. CHARACTER-LEVEL LANGUAGE MODELING WITH HIERARCHICAL RECURRENT NEURAL NETWORKS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1645

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