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ICASSP 2018

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.

IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES


A robust multi-view disparity estimation algorithm for noisy images is presented. The proposed algorithm constructs 3D focus image stacks (3DFIS) by projecting and stacking multi-view images and estimates a disparity map based on the 3DFIS. To make the algorithm robust to noise and occlusion, a texture-based view selection and patch size variation scheme based on texture map is proposed.

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Authors:
Shiwei Zhou, Zhengyang Lou, Yu Hen Hu, Hongrui Jiang
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12 April 2018 - 12:44pm
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[1] Shiwei Zhou, Zhengyang Lou, Yu Hen Hu, Hongrui Jiang, "IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2442. Accessed: Aug. 17, 2019.
@article{2442-18,
url = {http://sigport.org/2442},
author = {Shiwei Zhou; Zhengyang Lou; Yu Hen Hu; Hongrui Jiang },
publisher = {IEEE SigPort},
title = {IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES },
year = {2018} }
TY - EJOUR
T1 - IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES
AU - Shiwei Zhou; Zhengyang Lou; Yu Hen Hu; Hongrui Jiang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2442
ER -
Shiwei Zhou, Zhengyang Lou, Yu Hen Hu, Hongrui Jiang. (2018). IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES . IEEE SigPort. http://sigport.org/2442
Shiwei Zhou, Zhengyang Lou, Yu Hen Hu, Hongrui Jiang, 2018. IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES . Available at: http://sigport.org/2442.
Shiwei Zhou, Zhengyang Lou, Yu Hen Hu, Hongrui Jiang. (2018). "IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES ." Web.
1. Shiwei Zhou, Zhengyang Lou, Yu Hen Hu, Hongrui Jiang. IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2442

TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK

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Authors:
Meet H. Soni, Neil Shah, Hemant A. Patil
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12 April 2018 - 12:43pm
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Prof. Hemant A Patil_ICASSP18.pdf

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[1] Meet H. Soni, Neil Shah, Hemant A. Patil, "TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2441. Accessed: Aug. 17, 2019.
@article{2441-18,
url = {http://sigport.org/2441},
author = {Meet H. Soni; Neil Shah; Hemant A. Patil },
publisher = {IEEE SigPort},
title = {TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK},
year = {2018} }
TY - EJOUR
T1 - TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK
AU - Meet H. Soni; Neil Shah; Hemant A. Patil
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2441
ER -
Meet H. Soni, Neil Shah, Hemant A. Patil. (2018). TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK. IEEE SigPort. http://sigport.org/2441
Meet H. Soni, Neil Shah, Hemant A. Patil, 2018. TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK. Available at: http://sigport.org/2441.
Meet H. Soni, Neil Shah, Hemant A. Patil. (2018). "TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK." Web.
1. Meet H. Soni, Neil Shah, Hemant A. Patil. TIME-FREQUENCY MASKING-BASED SPEECH ENHANCEMENT USING GENERATIVE ADVERSARIAL NETWORK [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2441

Sequence Modeling in Unsupervised Single-channel Overlapped Speech Recognition


Unsupervised single-channel overlapped speech recognition is one
of the hardest problems in automatic speech recognition (ASR). The
problems can be modularized into three sub-problems: frame-wise
interpreting, sequence level speaker tracing and speech recognition.
Nevertheless, previous acoustic models formulate the correlation between sequential labels implicitly, which limit the modeling effect.
In this work, we include explicit models for the sequential label
correlation during training. This is relevant to models given by both

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12 April 2018 - 12:40pm
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cocktail icassp2018 oral slides_zhc00.pdf

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[1] , "Sequence Modeling in Unsupervised Single-channel Overlapped Speech Recognition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2440. Accessed: Aug. 17, 2019.
@article{2440-18,
url = {http://sigport.org/2440},
author = { },
publisher = {IEEE SigPort},
title = {Sequence Modeling in Unsupervised Single-channel Overlapped Speech Recognition},
year = {2018} }
TY - EJOUR
T1 - Sequence Modeling in Unsupervised Single-channel Overlapped Speech Recognition
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2440
ER -
. (2018). Sequence Modeling in Unsupervised Single-channel Overlapped Speech Recognition. IEEE SigPort. http://sigport.org/2440
, 2018. Sequence Modeling in Unsupervised Single-channel Overlapped Speech Recognition. Available at: http://sigport.org/2440.
. (2018). "Sequence Modeling in Unsupervised Single-channel Overlapped Speech Recognition." Web.
1. . Sequence Modeling in Unsupervised Single-channel Overlapped Speech Recognition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2440

OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION


Test Zone Search (TZS) is considered the current state-of-the-art fast Motion Estimation algorithm because it presents

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Authors:
Marcelo Porto, Bruno Zatt, Luciano Agostini, Guilherme Correa
Submitted On:
12 April 2018 - 12:44pm
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OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION.pdf

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[1] Marcelo Porto, Bruno Zatt, Luciano Agostini, Guilherme Correa, "OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2438. Accessed: Aug. 17, 2019.
@article{2438-18,
url = {http://sigport.org/2438},
author = {Marcelo Porto; Bruno Zatt; Luciano Agostini; Guilherme Correa },
publisher = {IEEE SigPort},
title = {OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION},
year = {2018} }
TY - EJOUR
T1 - OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION
AU - Marcelo Porto; Bruno Zatt; Luciano Agostini; Guilherme Correa
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2438
ER -
Marcelo Porto, Bruno Zatt, Luciano Agostini, Guilherme Correa. (2018). OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION. IEEE SigPort. http://sigport.org/2438
Marcelo Porto, Bruno Zatt, Luciano Agostini, Guilherme Correa, 2018. OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION. Available at: http://sigport.org/2438.
Marcelo Porto, Bruno Zatt, Luciano Agostini, Guilherme Correa. (2018). "OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION." Web.
1. Marcelo Porto, Bruno Zatt, Luciano Agostini, Guilherme Correa. OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2438

BRIDGENETS: STUDENT-TEACHER TRANSFER LEARNING BASED ON RECURSIVE NEURAL NETWORKS AND ITS APPLICATION TO DISTANT SPEECH RECOGNITION

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12 April 2018 - 12:38pm
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[1] , "BRIDGENETS: STUDENT-TEACHER TRANSFER LEARNING BASED ON RECURSIVE NEURAL NETWORKS AND ITS APPLICATION TO DISTANT SPEECH RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2437. Accessed: Aug. 17, 2019.
@article{2437-18,
url = {http://sigport.org/2437},
author = { },
publisher = {IEEE SigPort},
title = {BRIDGENETS: STUDENT-TEACHER TRANSFER LEARNING BASED ON RECURSIVE NEURAL NETWORKS AND ITS APPLICATION TO DISTANT SPEECH RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - BRIDGENETS: STUDENT-TEACHER TRANSFER LEARNING BASED ON RECURSIVE NEURAL NETWORKS AND ITS APPLICATION TO DISTANT SPEECH RECOGNITION
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2437
ER -
. (2018). BRIDGENETS: STUDENT-TEACHER TRANSFER LEARNING BASED ON RECURSIVE NEURAL NETWORKS AND ITS APPLICATION TO DISTANT SPEECH RECOGNITION. IEEE SigPort. http://sigport.org/2437
, 2018. BRIDGENETS: STUDENT-TEACHER TRANSFER LEARNING BASED ON RECURSIVE NEURAL NETWORKS AND ITS APPLICATION TO DISTANT SPEECH RECOGNITION. Available at: http://sigport.org/2437.
. (2018). "BRIDGENETS: STUDENT-TEACHER TRANSFER LEARNING BASED ON RECURSIVE NEURAL NETWORKS AND ITS APPLICATION TO DISTANT SPEECH RECOGNITION." Web.
1. . BRIDGENETS: STUDENT-TEACHER TRANSFER LEARNING BASED ON RECURSIVE NEURAL NETWORKS AND ITS APPLICATION TO DISTANT SPEECH RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2437

ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING

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12 April 2018 - 12:37pm
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[1] , "ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2435. Accessed: Aug. 17, 2019.
@article{2435-18,
url = {http://sigport.org/2435},
author = { },
publisher = {IEEE SigPort},
title = {ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING},
year = {2018} }
TY - EJOUR
T1 - ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2435
ER -
. (2018). ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING. IEEE SigPort. http://sigport.org/2435
, 2018. ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING. Available at: http://sigport.org/2435.
. (2018). "ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING." Web.
1. . ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2435

On Modular Training of Neural Acoustics-to-Word Model for LVCSR


End-to-end (E2E) automatic speech recognition (ASR) systems directly map acoustics to words using a unified model. Previous works
mostly focus on E2E training a single model which integrates acoustic and language model into a whole. Although E2E training benefits
from sequence modeling and simplified decoding pipelines, large
amount of transcribed acoustic data is usually required, and traditional acoustic and language modelling techniques cannot be utilized. In this paper, a novel modular training framework of E2E ASR

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12 April 2018 - 12:34pm
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[1] , "On Modular Training of Neural Acoustics-to-Word Model for LVCSR", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2434. Accessed: Aug. 17, 2019.
@article{2434-18,
url = {http://sigport.org/2434},
author = { },
publisher = {IEEE SigPort},
title = {On Modular Training of Neural Acoustics-to-Word Model for LVCSR},
year = {2018} }
TY - EJOUR
T1 - On Modular Training of Neural Acoustics-to-Word Model for LVCSR
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2434
ER -
. (2018). On Modular Training of Neural Acoustics-to-Word Model for LVCSR. IEEE SigPort. http://sigport.org/2434
, 2018. On Modular Training of Neural Acoustics-to-Word Model for LVCSR. Available at: http://sigport.org/2434.
. (2018). "On Modular Training of Neural Acoustics-to-Word Model for LVCSR." Web.
1. . On Modular Training of Neural Acoustics-to-Word Model for LVCSR [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2434

SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES


In this article, we propose a Bounded Component Analysis (BCA) approach for the separation of the convolutive mixtures of sparse sources. The corresponding algorithm is derived from a geometric objective function defined over a completely deterministic setting. Therefore, it is applicable to sources which can be independent or dependent in both space and time dimensions. We show that all global optima of the proposed objective are perfect separators. We also provide numerical examples to illustrate the performance of the algorithm.

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Authors:
Eren Babatas
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12 April 2018 - 12:32pm
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[1] Eren Babatas, "SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2433. Accessed: Aug. 17, 2019.
@article{2433-18,
url = {http://sigport.org/2433},
author = {Eren Babatas },
publisher = {IEEE SigPort},
title = {SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES},
year = {2018} }
TY - EJOUR
T1 - SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES
AU - Eren Babatas
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2433
ER -
Eren Babatas. (2018). SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES. IEEE SigPort. http://sigport.org/2433
Eren Babatas, 2018. SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES. Available at: http://sigport.org/2433.
Eren Babatas. (2018). "SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES." Web.
1. Eren Babatas. SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2433

ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER


We propose an adaptive visual target tracking algorithm based on Label-Consistent K-Singular Value Decomposition (LC-KSVD) dictionary learning. To construct target templates, local patch features are sampled from foreground and background of the target. LC-KSVD then is applied to these local patches to simultaneously estimate a set of low-dimension dictionary and classification parameters (CP). To track the target over time, a kernel particle filter (KPF) is proposed that integrates both local and global motion information of the target.

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Authors:
Jinlong Yang, Xiaoping Chen, Yu Hen Hu Jianjun Liu
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12 April 2018 - 12:25pm
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Poster-2119.pdf

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[1] Jinlong Yang, Xiaoping Chen, Yu Hen Hu Jianjun Liu, "ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2432. Accessed: Aug. 17, 2019.
@article{2432-18,
url = {http://sigport.org/2432},
author = {Jinlong Yang; Xiaoping Chen; Yu Hen Hu Jianjun Liu },
publisher = {IEEE SigPort},
title = {ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER},
year = {2018} }
TY - EJOUR
T1 - ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER
AU - Jinlong Yang; Xiaoping Chen; Yu Hen Hu Jianjun Liu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2432
ER -
Jinlong Yang, Xiaoping Chen, Yu Hen Hu Jianjun Liu. (2018). ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER. IEEE SigPort. http://sigport.org/2432
Jinlong Yang, Xiaoping Chen, Yu Hen Hu Jianjun Liu, 2018. ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER. Available at: http://sigport.org/2432.
Jinlong Yang, Xiaoping Chen, Yu Hen Hu Jianjun Liu. (2018). "ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER." Web.
1. Jinlong Yang, Xiaoping Chen, Yu Hen Hu Jianjun Liu. ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2432

CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE


In this work, we investigate mapping both natural language food and quantity descriptions to matching USDA database entries. We demonstrate that a convolutional neural network (CNN) model with a softmax layer on top to directly predict the most likely database matches outperforms our previous state-of-the-art approach of learning binary classification and subsequently ranking database entries using similarity scores with the learned embeddings.

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Authors:
Mandy Korpusik, James Glass
Submitted On:
12 April 2018 - 12:21pm
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[1] Mandy Korpusik, James Glass, "CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2431. Accessed: Aug. 17, 2019.
@article{2431-18,
url = {http://sigport.org/2431},
author = {Mandy Korpusik; James Glass },
publisher = {IEEE SigPort},
title = {CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE},
year = {2018} }
TY - EJOUR
T1 - CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE
AU - Mandy Korpusik; James Glass
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2431
ER -
Mandy Korpusik, James Glass. (2018). CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE. IEEE SigPort. http://sigport.org/2431
Mandy Korpusik, James Glass, 2018. CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE. Available at: http://sigport.org/2431.
Mandy Korpusik, James Glass. (2018). "CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE." Web.
1. Mandy Korpusik, James Glass. CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2431

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