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Neural network learning (MLR-NNLR)

Cofnet: Predict with Confidence


POSTER.pdf

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Authors:
Tung-yu Wu, Wing H. Wong, Chen-Yi Lee
Submitted On:
13 April 2018 - 5:23am
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[1] Tung-yu Wu, Wing H. Wong, Chen-Yi Lee, "Cofnet: Predict with Confidence", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2667. Accessed: Sep. 23, 2018.
@article{2667-18,
url = {http://sigport.org/2667},
author = {Tung-yu Wu; Wing H. Wong; Chen-Yi Lee },
publisher = {IEEE SigPort},
title = {Cofnet: Predict with Confidence},
year = {2018} }
TY - EJOUR
T1 - Cofnet: Predict with Confidence
AU - Tung-yu Wu; Wing H. Wong; Chen-Yi Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2667
ER -
Tung-yu Wu, Wing H. Wong, Chen-Yi Lee. (2018). Cofnet: Predict with Confidence. IEEE SigPort. http://sigport.org/2667
Tung-yu Wu, Wing H. Wong, Chen-Yi Lee, 2018. Cofnet: Predict with Confidence. Available at: http://sigport.org/2667.
Tung-yu Wu, Wing H. Wong, Chen-Yi Lee. (2018). "Cofnet: Predict with Confidence." Web.
1. Tung-yu Wu, Wing H. Wong, Chen-Yi Lee. Cofnet: Predict with Confidence [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2667

A Supervised STDP-Based Training Algorithm for Living Neural Networks


Neural networks have shown great potential in many applications like speech recognition, drug discovery, image classification, and object detection. Neural network models are inspired by biological neural networks, but they are optimized to perform machine learning tasks on digital computers.

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Authors:
Kevin Devincentis, Yao Xiao, Zubayer Ibne Ferdous, Xiaochen Guo, Zhiyuan Yan, Yevgeny Berdichevsky
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13 April 2018 - 1:39am
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[1] Kevin Devincentis, Yao Xiao, Zubayer Ibne Ferdous, Xiaochen Guo, Zhiyuan Yan, Yevgeny Berdichevsky, "A Supervised STDP-Based Training Algorithm for Living Neural Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2611. Accessed: Sep. 23, 2018.
@article{2611-18,
url = {http://sigport.org/2611},
author = {Kevin Devincentis; Yao Xiao; Zubayer Ibne Ferdous; Xiaochen Guo; Zhiyuan Yan; Yevgeny Berdichevsky },
publisher = {IEEE SigPort},
title = {A Supervised STDP-Based Training Algorithm for Living Neural Networks},
year = {2018} }
TY - EJOUR
T1 - A Supervised STDP-Based Training Algorithm for Living Neural Networks
AU - Kevin Devincentis; Yao Xiao; Zubayer Ibne Ferdous; Xiaochen Guo; Zhiyuan Yan; Yevgeny Berdichevsky
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2611
ER -
Kevin Devincentis, Yao Xiao, Zubayer Ibne Ferdous, Xiaochen Guo, Zhiyuan Yan, Yevgeny Berdichevsky. (2018). A Supervised STDP-Based Training Algorithm for Living Neural Networks. IEEE SigPort. http://sigport.org/2611
Kevin Devincentis, Yao Xiao, Zubayer Ibne Ferdous, Xiaochen Guo, Zhiyuan Yan, Yevgeny Berdichevsky, 2018. A Supervised STDP-Based Training Algorithm for Living Neural Networks. Available at: http://sigport.org/2611.
Kevin Devincentis, Yao Xiao, Zubayer Ibne Ferdous, Xiaochen Guo, Zhiyuan Yan, Yevgeny Berdichevsky. (2018). "A Supervised STDP-Based Training Algorithm for Living Neural Networks." Web.
1. Kevin Devincentis, Yao Xiao, Zubayer Ibne Ferdous, Xiaochen Guo, Zhiyuan Yan, Yevgeny Berdichevsky. A Supervised STDP-Based Training Algorithm for Living Neural Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2611

SOLVING LINEAR INVERSE PROBLEMS USING GAN PRIORS: AN ALGORITHM WITH PROVABLE GUARANTEES


In recent works, both sparsity-based methods as well as learning-based methods have proven to be successful in solving several challenging linear inverse problems. However, sparsity priors for natural signals and images suffer from poor discriminative capability, while learning-based methods seldom provide concrete theoretical guarantees. In this work, we advocate the idea of replacing hand-crafted priors, such as sparsity, with a Generative Adversarial Network (GAN) to solve linear inverse problems such as compressive sensing.

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Authors:
Viraj Shah, Chinmay Hegde
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12 April 2018 - 6:24pm
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[1] Viraj Shah, Chinmay Hegde, "SOLVING LINEAR INVERSE PROBLEMS USING GAN PRIORS: AN ALGORITHM WITH PROVABLE GUARANTEES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2511. Accessed: Sep. 23, 2018.
@article{2511-18,
url = {http://sigport.org/2511},
author = {Viraj Shah; Chinmay Hegde },
publisher = {IEEE SigPort},
title = {SOLVING LINEAR INVERSE PROBLEMS USING GAN PRIORS: AN ALGORITHM WITH PROVABLE GUARANTEES},
year = {2018} }
TY - EJOUR
T1 - SOLVING LINEAR INVERSE PROBLEMS USING GAN PRIORS: AN ALGORITHM WITH PROVABLE GUARANTEES
AU - Viraj Shah; Chinmay Hegde
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2511
ER -
Viraj Shah, Chinmay Hegde. (2018). SOLVING LINEAR INVERSE PROBLEMS USING GAN PRIORS: AN ALGORITHM WITH PROVABLE GUARANTEES. IEEE SigPort. http://sigport.org/2511
Viraj Shah, Chinmay Hegde, 2018. SOLVING LINEAR INVERSE PROBLEMS USING GAN PRIORS: AN ALGORITHM WITH PROVABLE GUARANTEES. Available at: http://sigport.org/2511.
Viraj Shah, Chinmay Hegde. (2018). "SOLVING LINEAR INVERSE PROBLEMS USING GAN PRIORS: AN ALGORITHM WITH PROVABLE GUARANTEES." Web.
1. Viraj Shah, Chinmay Hegde. SOLVING LINEAR INVERSE PROBLEMS USING GAN PRIORS: AN ALGORITHM WITH PROVABLE GUARANTEES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2511

Learning convolutional sparse coding


We propose a convolutional recurrent sparse auto-encoder
model. The model consists of a sparse encoder, which is a
convolutional extension of the learned ISTA (LISTA) method,
and a linear convolutional decoder. Our strategy offers a simple
method for learning a task-driven sparse convolutional
dictionary (CD), and producing an approximate convolutional
sparse code (CSC) over the learned dictionary. We trained
the model to minimize reconstruction loss via gradient decent
with back-propagation and have achieved competitve

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Authors:
Hillel Sreter, Raja Giryes
Submitted On:
20 April 2018 - 12:42pm
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CSC - ICASSP.pptx

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CSC - ICASSP.pdf

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[1] Hillel Sreter, Raja Giryes, "Learning convolutional sparse coding ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2478. Accessed: Sep. 23, 2018.
@article{2478-18,
url = {http://sigport.org/2478},
author = {Hillel Sreter; Raja Giryes },
publisher = {IEEE SigPort},
title = {Learning convolutional sparse coding },
year = {2018} }
TY - EJOUR
T1 - Learning convolutional sparse coding
AU - Hillel Sreter; Raja Giryes
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2478
ER -
Hillel Sreter, Raja Giryes. (2018). Learning convolutional sparse coding . IEEE SigPort. http://sigport.org/2478
Hillel Sreter, Raja Giryes, 2018. Learning convolutional sparse coding . Available at: http://sigport.org/2478.
Hillel Sreter, Raja Giryes. (2018). "Learning convolutional sparse coding ." Web.
1. Hillel Sreter, Raja Giryes. Learning convolutional sparse coding [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2478

Document Quality Estimation using Spatial Frequency Response


The current Document Image Quality Assessment (DIQA) algorithms directly relate the Optical Character Recognition (OCR) accuracies with the quality of the document to build supervised learning frameworks. This direct correlation has two major limitations: (a) OCR may be affected by factors independent of the quality of the capture and (b) it cannot account for blur variations within an image. An alternate possibility is to quantify the quality of capture using human judgement, however, it is subjective and prone to error.

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Authors:
Pranjal Kumar Rai, Sajal Maheshwari, Vineet Gandhi
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13 April 2018 - 2:24am
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[1] Pranjal Kumar Rai, Sajal Maheshwari, Vineet Gandhi, "Document Quality Estimation using Spatial Frequency Response", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2469. Accessed: Sep. 23, 2018.
@article{2469-18,
url = {http://sigport.org/2469},
author = {Pranjal Kumar Rai; Sajal Maheshwari; Vineet Gandhi },
publisher = {IEEE SigPort},
title = {Document Quality Estimation using Spatial Frequency Response},
year = {2018} }
TY - EJOUR
T1 - Document Quality Estimation using Spatial Frequency Response
AU - Pranjal Kumar Rai; Sajal Maheshwari; Vineet Gandhi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2469
ER -
Pranjal Kumar Rai, Sajal Maheshwari, Vineet Gandhi. (2018). Document Quality Estimation using Spatial Frequency Response. IEEE SigPort. http://sigport.org/2469
Pranjal Kumar Rai, Sajal Maheshwari, Vineet Gandhi, 2018. Document Quality Estimation using Spatial Frequency Response. Available at: http://sigport.org/2469.
Pranjal Kumar Rai, Sajal Maheshwari, Vineet Gandhi. (2018). "Document Quality Estimation using Spatial Frequency Response." Web.
1. Pranjal Kumar Rai, Sajal Maheshwari, Vineet Gandhi. Document Quality Estimation using Spatial Frequency Response [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2469

AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING


Magnetic resonance (MR) plays an important role in medical imaging. It can be flexibly tuned towards different applications for deriving a meaningful diagnosis. However, its long acquisition times and flexible parametrization make it on the other hand prone to artifacts which obscure the underlying image content or can be misinterpreted as anatomy. Patient-induced motion artifacts are still one of the major extrinsic factors which degrade image quality.

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Authors:
Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang
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12 April 2018 - 12:45pm
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[1] Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang, "AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2443. Accessed: Sep. 23, 2018.
@article{2443-18,
url = {http://sigport.org/2443},
author = {Thomas Küstner; Marvin Jandt; Annika Liebgott; Lukas Mauch; Petros Martirosian; Fabian Bamberg; Konstantin Nikolaou; Sergios Gatidis; Fritz Schick; Bin Yang },
publisher = {IEEE SigPort},
title = {AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING},
year = {2018} }
TY - EJOUR
T1 - AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING
AU - Thomas Küstner; Marvin Jandt; Annika Liebgott; Lukas Mauch; Petros Martirosian; Fabian Bamberg; Konstantin Nikolaou; Sergios Gatidis; Fritz Schick; Bin Yang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2443
ER -
Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang. (2018). AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING. IEEE SigPort. http://sigport.org/2443
Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang, 2018. AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING. Available at: http://sigport.org/2443.
Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang. (2018). "AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING." Web.
1. Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang. AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2443

Speaker Diarization with LSTM


For many years, i-vector based audio embedding techniques were the dominant approach for speaker verification and speaker diarization applications. However, mirroring the rise of deep learning in various domains, neural network based audio embeddings, also known as d-vectors, have consistently demonstrated superior speaker verification performance. In this paper, we build on the success of d-vector based speaker verification systems to develop a new d-vector based approach to speaker diarization.

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Authors:
Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno
Submitted On:
12 April 2018 - 11:54am
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[1] Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno, "Speaker Diarization with LSTM", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2421. Accessed: Sep. 23, 2018.
@article{2421-18,
url = {http://sigport.org/2421},
author = {Quan Wang; Carlton Downey; Li Wan; Philip Andrew Mansfield; Ignacio Lopez Moreno },
publisher = {IEEE SigPort},
title = {Speaker Diarization with LSTM},
year = {2018} }
TY - EJOUR
T1 - Speaker Diarization with LSTM
AU - Quan Wang; Carlton Downey; Li Wan; Philip Andrew Mansfield; Ignacio Lopez Moreno
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2421
ER -
Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno. (2018). Speaker Diarization with LSTM. IEEE SigPort. http://sigport.org/2421
Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno, 2018. Speaker Diarization with LSTM. Available at: http://sigport.org/2421.
Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno. (2018). "Speaker Diarization with LSTM." Web.
1. Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno. Speaker Diarization with LSTM [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2421

ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION


Attention-based models have recently shown great performance on a range of tasks, such as speech recognition, machine translation, and image captioning due to their ability to summarize relevant information that expands through the entire length of an input sequence. In this paper, we analyze the usage of attention mechanisms to the problem of sequence summarization in our end-to-end text-dependent speaker recognition system. We explore different topologies and their variants of the attention layer, and compare different pooling methods on the attention weights.

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Authors:
F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan
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12 April 2018 - 11:42am
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[1] F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan, "ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2413. Accessed: Sep. 23, 2018.
@article{2413-18,
url = {http://sigport.org/2413},
author = {F A Rezaur Rahman Chowdhury; Quan Wang; Ignacio Lopez Moreno; Li Wan },
publisher = {IEEE SigPort},
title = {ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION},
year = {2018} }
TY - EJOUR
T1 - ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION
AU - F A Rezaur Rahman Chowdhury; Quan Wang; Ignacio Lopez Moreno; Li Wan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2413
ER -
F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan. (2018). ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION. IEEE SigPort. http://sigport.org/2413
F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan, 2018. ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION. Available at: http://sigport.org/2413.
F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan. (2018). "ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION." Web.
1. F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan. ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2413

Improving the Capacity of Very Deep Networks with Maxout Units


Deep neural networks inherently have large representational power for approximating complex target functions. However,

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Authors:
Oyebade Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Björn Ottersten
Submitted On:
12 April 2018 - 11:43am
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[1] Oyebade Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Björn Ottersten, "Improving the Capacity of Very Deep Networks with Maxout Units", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2410. Accessed: Sep. 23, 2018.
@article{2410-18,
url = {http://sigport.org/2410},
author = {Oyebade Oyedotun; Abd El Rahman Shabayek; Djamila Aouada; Björn Ottersten },
publisher = {IEEE SigPort},
title = {Improving the Capacity of Very Deep Networks with Maxout Units},
year = {2018} }
TY - EJOUR
T1 - Improving the Capacity of Very Deep Networks with Maxout Units
AU - Oyebade Oyedotun; Abd El Rahman Shabayek; Djamila Aouada; Björn Ottersten
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2410
ER -
Oyebade Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Björn Ottersten. (2018). Improving the Capacity of Very Deep Networks with Maxout Units. IEEE SigPort. http://sigport.org/2410
Oyebade Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Björn Ottersten, 2018. Improving the Capacity of Very Deep Networks with Maxout Units. Available at: http://sigport.org/2410.
Oyebade Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Björn Ottersten. (2018). "Improving the Capacity of Very Deep Networks with Maxout Units." Web.
1. Oyebade Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Björn Ottersten. Improving the Capacity of Very Deep Networks with Maxout Units [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2410

HOW SAMPLING RATE AFFECTS CROSS-DOMAIN TRANSFER LEARNING FOR VIDEO DESCRIPTION

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Submitted On:
12 April 2018 - 11:10am
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[1] , "HOW SAMPLING RATE AFFECTS CROSS-DOMAIN TRANSFER LEARNING FOR VIDEO DESCRIPTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2380. Accessed: Sep. 23, 2018.
@article{2380-18,
url = {http://sigport.org/2380},
author = { },
publisher = {IEEE SigPort},
title = {HOW SAMPLING RATE AFFECTS CROSS-DOMAIN TRANSFER LEARNING FOR VIDEO DESCRIPTION},
year = {2018} }
TY - EJOUR
T1 - HOW SAMPLING RATE AFFECTS CROSS-DOMAIN TRANSFER LEARNING FOR VIDEO DESCRIPTION
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2380
ER -
. (2018). HOW SAMPLING RATE AFFECTS CROSS-DOMAIN TRANSFER LEARNING FOR VIDEO DESCRIPTION. IEEE SigPort. http://sigport.org/2380
, 2018. HOW SAMPLING RATE AFFECTS CROSS-DOMAIN TRANSFER LEARNING FOR VIDEO DESCRIPTION. Available at: http://sigport.org/2380.
. (2018). "HOW SAMPLING RATE AFFECTS CROSS-DOMAIN TRANSFER LEARNING FOR VIDEO DESCRIPTION." Web.
1. . HOW SAMPLING RATE AFFECTS CROSS-DOMAIN TRANSFER LEARNING FOR VIDEO DESCRIPTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2380

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