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Other applications of machine learning (MLR-APPL)

COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION


Deep Neural Network (DNN) is a basic method used for the rare Acoustic Event Detection (AED) in synthesised audio. The structure of DNNs including Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN) for AED tasks has rather fewer hidden layers compared with computer vision systems. This paper tries to demonstrate that a DNN with more hidden layers does not necessarily guarantee a better performance in AED tasks.

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Authors:
Shengchen Li
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19 April 2018 - 9:58pm
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COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION.pdf

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[1] Shengchen Li, "COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3053. Accessed: Apr. 23, 2018.
@article{3053-18,
url = {http://sigport.org/3053},
author = {Shengchen Li },
publisher = {IEEE SigPort},
title = {COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION},
year = {2018} }
TY - EJOUR
T1 - COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION
AU - Shengchen Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3053
ER -
Shengchen Li. (2018). COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION. IEEE SigPort. http://sigport.org/3053
Shengchen Li, 2018. COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION. Available at: http://sigport.org/3053.
Shengchen Li. (2018). "COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION." Web.
1. Shengchen Li. COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3053

The Landscape of Non-convex Quadratic Feasibility


Motivated by applications such as ordinal embedding and collaborative ranking, we formulate homogeneous quadratic feasibility as an unconstrained, non-convex minimization problem. Our work aims to understand the landscape (local minimizers and global minimizers) of the non-convex objective, which corresponds to hinge losses arising from quadratic constraints. Under certain assumptions, we give necessary conditions for non-global, local minimizers of our objective and additionally show that in two dimensions, every local minimizer is a global minimizer.

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Authors:
Lalit Jain, Laura Balzano
Submitted On:
19 April 2018 - 2:10pm
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ICASSP_v4.pdf

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[1] Lalit Jain, Laura Balzano, "The Landscape of Non-convex Quadratic Feasibility", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2798. Accessed: Apr. 23, 2018.
@article{2798-18,
url = {http://sigport.org/2798},
author = {Lalit Jain; Laura Balzano },
publisher = {IEEE SigPort},
title = {The Landscape of Non-convex Quadratic Feasibility},
year = {2018} }
TY - EJOUR
T1 - The Landscape of Non-convex Quadratic Feasibility
AU - Lalit Jain; Laura Balzano
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2798
ER -
Lalit Jain, Laura Balzano. (2018). The Landscape of Non-convex Quadratic Feasibility. IEEE SigPort. http://sigport.org/2798
Lalit Jain, Laura Balzano, 2018. The Landscape of Non-convex Quadratic Feasibility. Available at: http://sigport.org/2798.
Lalit Jain, Laura Balzano. (2018). "The Landscape of Non-convex Quadratic Feasibility." Web.
1. Lalit Jain, Laura Balzano. The Landscape of Non-convex Quadratic Feasibility [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2798

RFCM for Data Association and Multitarget Tracking Using 3D Radar


erformance of object classification using 3D automotive radar relies on accurate data association and multitarget tracking, which are greatly affected by data bias and proximity of objects to each other. A regularized fuzzy c-means (RFCM) algorithm is proposed herein to resolve the data association uncertainty problem that has shown to outperform the conventional FCM algorithm. The proposed method exploits results from the companion tracker to increase performance robustness. Simulation results using simulated and field data have proven the efficacy of the proposed method.

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Authors:
Chun-Nien Chan, Carrson C. Fung
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13 April 2018 - 11:39am
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ICASSP 2018 poster

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[1] Chun-Nien Chan, Carrson C. Fung, "RFCM for Data Association and Multitarget Tracking Using 3D Radar", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2721. Accessed: Apr. 23, 2018.
@article{2721-18,
url = {http://sigport.org/2721},
author = {Chun-Nien Chan; Carrson C. Fung },
publisher = {IEEE SigPort},
title = {RFCM for Data Association and Multitarget Tracking Using 3D Radar},
year = {2018} }
TY - EJOUR
T1 - RFCM for Data Association and Multitarget Tracking Using 3D Radar
AU - Chun-Nien Chan; Carrson C. Fung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2721
ER -
Chun-Nien Chan, Carrson C. Fung. (2018). RFCM for Data Association and Multitarget Tracking Using 3D Radar. IEEE SigPort. http://sigport.org/2721
Chun-Nien Chan, Carrson C. Fung, 2018. RFCM for Data Association and Multitarget Tracking Using 3D Radar. Available at: http://sigport.org/2721.
Chun-Nien Chan, Carrson C. Fung. (2018). "RFCM for Data Association and Multitarget Tracking Using 3D Radar." Web.
1. Chun-Nien Chan, Carrson C. Fung. RFCM for Data Association and Multitarget Tracking Using 3D Radar [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2721

EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY


Dynamic functional connectivity has become a prominent approach for tracking the changes of macroscale statistical dependencies between regions in the brain. Effective parametrization of these statistical dependencies, referred to as brain states, is however still an open problem. We investigate different emission models in the hidden Markov model framework, each representing certain assumptions about dynamic changes in the brain.

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Authors:
Søren Føns Vind Nielsen, Yuri Levin-Schwartz, Diego Vidaurre, Tulay Adali, Vince D. Calhoun, Kristoffer H. Madsen, Lars Kai Hansen, Morten Mørup
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13 April 2018 - 10:02am
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posterICASSP2018.pdf

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[1] Søren Føns Vind Nielsen, Yuri Levin-Schwartz, Diego Vidaurre, Tulay Adali, Vince D. Calhoun, Kristoffer H. Madsen, Lars Kai Hansen, Morten Mørup, "EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2707. Accessed: Apr. 23, 2018.
@article{2707-18,
url = {http://sigport.org/2707},
author = {Søren Føns Vind Nielsen; Yuri Levin-Schwartz; Diego Vidaurre; Tulay Adali; Vince D. Calhoun; Kristoffer H. Madsen; Lars Kai Hansen; Morten Mørup },
publisher = {IEEE SigPort},
title = {EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY},
year = {2018} }
TY - EJOUR
T1 - EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY
AU - Søren Føns Vind Nielsen; Yuri Levin-Schwartz; Diego Vidaurre; Tulay Adali; Vince D. Calhoun; Kristoffer H. Madsen; Lars Kai Hansen; Morten Mørup
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2707
ER -
Søren Føns Vind Nielsen, Yuri Levin-Schwartz, Diego Vidaurre, Tulay Adali, Vince D. Calhoun, Kristoffer H. Madsen, Lars Kai Hansen, Morten Mørup. (2018). EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY. IEEE SigPort. http://sigport.org/2707
Søren Føns Vind Nielsen, Yuri Levin-Schwartz, Diego Vidaurre, Tulay Adali, Vince D. Calhoun, Kristoffer H. Madsen, Lars Kai Hansen, Morten Mørup, 2018. EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY. Available at: http://sigport.org/2707.
Søren Føns Vind Nielsen, Yuri Levin-Schwartz, Diego Vidaurre, Tulay Adali, Vince D. Calhoun, Kristoffer H. Madsen, Lars Kai Hansen, Morten Mørup. (2018). "EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY." Web.
1. Søren Føns Vind Nielsen, Yuri Levin-Schwartz, Diego Vidaurre, Tulay Adali, Vince D. Calhoun, Kristoffer H. Madsen, Lars Kai Hansen, Morten Mørup. EVALUATING MODELS OF DYNAMIC FUNCTIONAL CONNECTIVITY USING PREDICTIVE CLASSIFICATION ACCURACY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2707

DNN-based Wireless Positioning in an Outdoor Environment


In this paper, we propose a deep learning based algorithm to estimate the position of an user by utilizing reference signal received power (RSRP) and the location of base stations. To obtain reliable results in a real communication environment, parameters were measured using commercially available base stations and mobile phones within a LTE network. Since the structure of the measured data changes in accordance with the number of connected base stations, it is necessary to work on data uniformity processing before running the deep learning network.

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Authors:
Chahyeon Eom, Youngsu Kwak, Hong-Goo Kang, Chungyong Lee
Submitted On:
13 April 2018 - 4:28am
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[1] Chahyeon Eom, Youngsu Kwak, Hong-Goo Kang, Chungyong Lee, "DNN-based Wireless Positioning in an Outdoor Environment", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2652. Accessed: Apr. 23, 2018.
@article{2652-18,
url = {http://sigport.org/2652},
author = {Chahyeon Eom; Youngsu Kwak; Hong-Goo Kang; Chungyong Lee },
publisher = {IEEE SigPort},
title = {DNN-based Wireless Positioning in an Outdoor Environment},
year = {2018} }
TY - EJOUR
T1 - DNN-based Wireless Positioning in an Outdoor Environment
AU - Chahyeon Eom; Youngsu Kwak; Hong-Goo Kang; Chungyong Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2652
ER -
Chahyeon Eom, Youngsu Kwak, Hong-Goo Kang, Chungyong Lee. (2018). DNN-based Wireless Positioning in an Outdoor Environment. IEEE SigPort. http://sigport.org/2652
Chahyeon Eom, Youngsu Kwak, Hong-Goo Kang, Chungyong Lee, 2018. DNN-based Wireless Positioning in an Outdoor Environment. Available at: http://sigport.org/2652.
Chahyeon Eom, Youngsu Kwak, Hong-Goo Kang, Chungyong Lee. (2018). "DNN-based Wireless Positioning in an Outdoor Environment." Web.
1. Chahyeon Eom, Youngsu Kwak, Hong-Goo Kang, Chungyong Lee. DNN-based Wireless Positioning in an Outdoor Environment [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2652

Nonnegative Matrix Factorization with Transform Learning


Traditional NMF-based signal decomposition relies on the factorization of spectral data, which is typically computed by means of short-time frequency transform. In this paper we propose to relax the choice of a pre-fixed transform and learn a short-time orthogonal transform together with the factorization. To this end, we formulate a regularized optimization problem reminiscent of conventional NMF, yet with the transform as additional unknown parameters, and design a novel block-descent algorithm enabling to find stationary points of this objective function.

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Authors:
Dylan Fagot, Herwig Wendt, Cédric Févotte
Submitted On:
12 April 2018 - 4:53pm
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[1] Dylan Fagot, Herwig Wendt, Cédric Févotte, "Nonnegative Matrix Factorization with Transform Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2501. Accessed: Apr. 23, 2018.
@article{2501-18,
url = {http://sigport.org/2501},
author = {Dylan Fagot; Herwig Wendt; Cédric Févotte },
publisher = {IEEE SigPort},
title = {Nonnegative Matrix Factorization with Transform Learning},
year = {2018} }
TY - EJOUR
T1 - Nonnegative Matrix Factorization with Transform Learning
AU - Dylan Fagot; Herwig Wendt; Cédric Févotte
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2501
ER -
Dylan Fagot, Herwig Wendt, Cédric Févotte. (2018). Nonnegative Matrix Factorization with Transform Learning. IEEE SigPort. http://sigport.org/2501
Dylan Fagot, Herwig Wendt, Cédric Févotte, 2018. Nonnegative Matrix Factorization with Transform Learning. Available at: http://sigport.org/2501.
Dylan Fagot, Herwig Wendt, Cédric Févotte. (2018). "Nonnegative Matrix Factorization with Transform Learning." Web.
1. Dylan Fagot, Herwig Wendt, Cédric Févotte. Nonnegative Matrix Factorization with Transform Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2501

OPEN SET RECOGNITION BY REGULARISING CLASSIFIER WITH FAKE DATA GENERATED BY GENERATIVE ADVERSARIAL NETWORKS


We present a new method to generate fake data in unknown classes in generative adversarial networks (GANs) framework. The generator in GANs is trained to generate somewhat similar to data in known classes but the different one by modelling noisy distribution on feature space of a classifier using proposed marginal denoising autoencoder. The generated data are treated as fake instances in unknown classes and given to the classifier to make it be robust to the real unknown classes.

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Authors:
Inhyuk Jo, Jungtaek Kim, Hyohyeong Kang, Yong-Deok Kim, Seungjin Choi
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12 April 2018 - 4:21pm
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[1] Inhyuk Jo, Jungtaek Kim, Hyohyeong Kang, Yong-Deok Kim, Seungjin Choi, "OPEN SET RECOGNITION BY REGULARISING CLASSIFIER WITH FAKE DATA GENERATED BY GENERATIVE ADVERSARIAL NETWORKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2494. Accessed: Apr. 23, 2018.
@article{2494-18,
url = {http://sigport.org/2494},
author = {Inhyuk Jo; Jungtaek Kim; Hyohyeong Kang; Yong-Deok Kim; Seungjin Choi },
publisher = {IEEE SigPort},
title = {OPEN SET RECOGNITION BY REGULARISING CLASSIFIER WITH FAKE DATA GENERATED BY GENERATIVE ADVERSARIAL NETWORKS},
year = {2018} }
TY - EJOUR
T1 - OPEN SET RECOGNITION BY REGULARISING CLASSIFIER WITH FAKE DATA GENERATED BY GENERATIVE ADVERSARIAL NETWORKS
AU - Inhyuk Jo; Jungtaek Kim; Hyohyeong Kang; Yong-Deok Kim; Seungjin Choi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2494
ER -
Inhyuk Jo, Jungtaek Kim, Hyohyeong Kang, Yong-Deok Kim, Seungjin Choi. (2018). OPEN SET RECOGNITION BY REGULARISING CLASSIFIER WITH FAKE DATA GENERATED BY GENERATIVE ADVERSARIAL NETWORKS. IEEE SigPort. http://sigport.org/2494
Inhyuk Jo, Jungtaek Kim, Hyohyeong Kang, Yong-Deok Kim, Seungjin Choi, 2018. OPEN SET RECOGNITION BY REGULARISING CLASSIFIER WITH FAKE DATA GENERATED BY GENERATIVE ADVERSARIAL NETWORKS. Available at: http://sigport.org/2494.
Inhyuk Jo, Jungtaek Kim, Hyohyeong Kang, Yong-Deok Kim, Seungjin Choi. (2018). "OPEN SET RECOGNITION BY REGULARISING CLASSIFIER WITH FAKE DATA GENERATED BY GENERATIVE ADVERSARIAL NETWORKS." Web.
1. Inhyuk Jo, Jungtaek Kim, Hyohyeong Kang, Yong-Deok Kim, Seungjin Choi. OPEN SET RECOGNITION BY REGULARISING CLASSIFIER WITH FAKE DATA GENERATED BY GENERATIVE ADVERSARIAL NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2494

A feature fusion method based on extreme learning machine for speech emotion recognition


Speech emotion recognition is important to understand users' intention in human-computer interaction. However, it is a challenging task partly because we cannot clearly know which feature and model are effective to distinguish emotions. Previous studies utilize convolutional neural network (CNN) directly on spectrograms to extract features, and bidirectional long short term memory (BLSTM) is the state-of-the-art model. However, there are two problems of CNN-BLSTM. Firstly, it doesn't utilize heuristic features based on priori knowledge.

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Authors:
Longbiao Wang, Jianwu Dang, Linjuan Zhang, Haotian Guan
Submitted On:
12 April 2018 - 12:07pm
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poster-guolili.pdf

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[1] Longbiao Wang, Jianwu Dang, Linjuan Zhang, Haotian Guan, "A feature fusion method based on extreme learning machine for speech emotion recognition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2426. Accessed: Apr. 23, 2018.
@article{2426-18,
url = {http://sigport.org/2426},
author = {Longbiao Wang; Jianwu Dang; Linjuan Zhang; Haotian Guan },
publisher = {IEEE SigPort},
title = {A feature fusion method based on extreme learning machine for speech emotion recognition},
year = {2018} }
TY - EJOUR
T1 - A feature fusion method based on extreme learning machine for speech emotion recognition
AU - Longbiao Wang; Jianwu Dang; Linjuan Zhang; Haotian Guan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2426
ER -
Longbiao Wang, Jianwu Dang, Linjuan Zhang, Haotian Guan. (2018). A feature fusion method based on extreme learning machine for speech emotion recognition. IEEE SigPort. http://sigport.org/2426
Longbiao Wang, Jianwu Dang, Linjuan Zhang, Haotian Guan, 2018. A feature fusion method based on extreme learning machine for speech emotion recognition. Available at: http://sigport.org/2426.
Longbiao Wang, Jianwu Dang, Linjuan Zhang, Haotian Guan. (2018). "A feature fusion method based on extreme learning machine for speech emotion recognition." Web.
1. Longbiao Wang, Jianwu Dang, Linjuan Zhang, Haotian Guan. A feature fusion method based on extreme learning machine for speech emotion recognition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2426

Performance Benchmarks for Detection Problems


We propose a benchmark curve that measures the inherent complexity of a detection problem. The benchmark curve is built using a sequence of simple detection methods based upon random projection. It is parameterized by the area above the receiver-operating characteristic curve of the detection method and its computational cost. It divides the plane into regions that can be used to characterize the computational and structural advantages of a given detection method. Numerical illustrations are provided.

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Authors:
Kelsie Larson, Mireille Boutin
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13 November 2017 - 12:58am
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Performance Benchmarks for Detection Problems

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[1] Kelsie Larson, Mireille Boutin, "Performance Benchmarks for Detection Problems", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2332. Accessed: Apr. 23, 2018.
@article{2332-17,
url = {http://sigport.org/2332},
author = {Kelsie Larson; Mireille Boutin },
publisher = {IEEE SigPort},
title = {Performance Benchmarks for Detection Problems},
year = {2017} }
TY - EJOUR
T1 - Performance Benchmarks for Detection Problems
AU - Kelsie Larson; Mireille Boutin
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2332
ER -
Kelsie Larson, Mireille Boutin. (2017). Performance Benchmarks for Detection Problems. IEEE SigPort. http://sigport.org/2332
Kelsie Larson, Mireille Boutin, 2017. Performance Benchmarks for Detection Problems. Available at: http://sigport.org/2332.
Kelsie Larson, Mireille Boutin. (2017). "Performance Benchmarks for Detection Problems." Web.
1. Kelsie Larson, Mireille Boutin. Performance Benchmarks for Detection Problems [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2332

A 200MHZ 202.4GFLOPS@10.8W VGG16 ACCELERATOR IN XILINX VX690T

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10 November 2017 - 9:03am
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globalSIP.pdf

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[1] , "A 200MHZ 202.4GFLOPS@10.8W VGG16 ACCELERATOR IN XILINX VX690T", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2291. Accessed: Apr. 23, 2018.
@article{2291-17,
url = {http://sigport.org/2291},
author = { },
publisher = {IEEE SigPort},
title = {A 200MHZ 202.4GFLOPS@10.8W VGG16 ACCELERATOR IN XILINX VX690T},
year = {2017} }
TY - EJOUR
T1 - A 200MHZ 202.4GFLOPS@10.8W VGG16 ACCELERATOR IN XILINX VX690T
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2291
ER -
. (2017). A 200MHZ 202.4GFLOPS@10.8W VGG16 ACCELERATOR IN XILINX VX690T. IEEE SigPort. http://sigport.org/2291
, 2017. A 200MHZ 202.4GFLOPS@10.8W VGG16 ACCELERATOR IN XILINX VX690T. Available at: http://sigport.org/2291.
. (2017). "A 200MHZ 202.4GFLOPS@10.8W VGG16 ACCELERATOR IN XILINX VX690T." Web.
1. . A 200MHZ 202.4GFLOPS@10.8W VGG16 ACCELERATOR IN XILINX VX690T [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2291

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