Result for "deep neural networks",146

NTT Communication Science Laboratories Open House 2018

Exhibition Program 5 Memory efficient deep learning for mobile devices Quantized neural networks for model

https://www.rd.ntt/cs/event/openhouse/2018/exhibition/5/index_en.html

NTT Communication Science Laboratories Open House 2018

trained layered neural networks Abstract The effectiveness of layered neural networks is widely

https://www.rd.ntt/cs/event/openhouse/2018/exhibition/7/index_en.html

スライド 1

effectiveness of layered neural networks is widely acknowledged for a wide range of tasks, including image

https://www.rd.ntt/cs/event/openhouse/2018/exhibition/7/poster_en_7.pdf

Tomoyasu Horikawa | NTT R&D Website

). Keywords brain decoding, deep neural networks, functional magnetic resonance imaging (fMRI), mental imagery

https://www.rd.ntt/e/organization/researcher/special/s_077.html

スライド 1

スライド 1 Contact References Abstract Copyright (C) 2018 NTT corp. All Rights Reserved. Deep Neural

https://www.rd.ntt/cs/event/openhouse/2018/exhibition/5/poster_en_5.pdf

NTT Communication Science Laboratories Open House 2018 Program

environmental sensing and machine learning Memory efficient deep learning for mobile devices Quantized neural

https://www.rd.ntt/cs/event/openhouse/2018/program_en.html

NTT Communication Science Laboratories Open House 2018

Quantized neural networks for model compression Optics makes machine learning much faster Photonic reservoir

https://www.rd.ntt/cs/event/openhouse/2018/en_simple/index_en.html

NTT Communication Science Laboratories Open House 2016

walking, train ,and car by utilizing deep neural networks (DNNs) that automatically extract movement

https://www.rd.ntt/cs/event/openhouse/2016/exhibition/5/index_en.html

poster_en.pdf

transportation modes such as walking, train ,and car by utilizing deep neural networks (DNNs) that automatically

https://www.rd.ntt/cs/event/openhouse/2016/exhibition/5/poster_en.pdf

poster_en_20.pdf

., “Context adaptive deep neural networks for fast acoustic model adaptation,” in Proc. ICASSP, 2015 [2

https://www.rd.ntt/cs/event/openhouse/2017/exhibition/20/poster_en_20.pdf

Yasutoshi Ida | NTT R&D Website

Based Preconditioning for Deep Neural Networks", International Joint Conference on Artificial

https://www.rd.ntt/e/organization/researcher/special/s_064.html

Deep Image Generation Based on Optics and Physics | NTT Communication Science Laboratories | NTT R&D Website

are composed of black-box neural networks and do not always generate images that are optically or

https://www.rd.ntt/e/cs/team_project/media/recognition/research_media21.html

Problem Solving Will Not Reduce the Number of Research Themes―It Will Open up New Research Areas | NTT R&D Website

artificial intelligence (AI) started around 2012, since then, neural networks based on deep learning have

https://www.rd.ntt/e/research/JN202203_17523.html

スライド 1

characteristics ・Block online processing 17 Who spoke when & what? How many people were there? - All-neural source

https://www.rd.ntt/cs/event/openhouse/2019/download/17_c_en.pdf

poster_en.pdf

fields by training deep neural networks. Our algorithm adapts learning rate by using directions of past

https://www.rd.ntt/cs/event/openhouse/2016/exhibition/7/poster_en.pdf

Smart Traffic Coordination via Learnable Digital Twins-Future Possibilities of Distributed Deep Learning | NTT R&D Website

decentralized federated learning project aims to train model parameters (e.g., in neural networks) under a

https://www.rd.ntt/e/research/JN202208_19150.html

poster_en18.pdf

Laboratory [1] Y. Kubo, T. Hori, A. Nakamura, “Integrating deep neural networks into structured

https://www.rd.ntt/cs/event/openhouse/2013/exhibition/media6/poster_en18.pdf

C20_パネル一覧0501

, K. Kinoshita, T. Hori, T. Nakatani, “Context adaptive deep neural networks for fast acoustic model

https://www.rd.ntt/cs/event/openhouse/2017/exhibition/20/poster20.pdf

スライド 1

, “Modular representation of layered neural networks,” Neural Networks, Vol. 97, pp. 62-73, 2018. [2] 渡邊千紘，平松

https://www.rd.ntt/cs/event/openhouse/2018/exhibition/7/poster7.pdf

poster_en_16.pdf

, “Adaptive visual feedback generation for facial expression improvement with multi-task deep neural networks

https://www.rd.ntt/cs/event/openhouse/2017/exhibition/16/poster_en_16.pdf