Result for "deep neural networks",147

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

poster18.pdf

, A. Nakamura, “Integrating deep neural networks into structured classification approach based on

https://www.rd.ntt/cs/event/openhouse/2013/exhibition/media6/poster18.pdf

NTT Communication Science Laboratories Open House 2013 Transcribing every word whoever speaks - Speech recognizers robust to casual speech variations -

the full-size PDF file. Reference Y. Kubo, T. Hori, A. Nakamura, “Integrating deep neural networks

https://www.rd.ntt/cs/event/openhouse/2013/exhibition/media6/index_en.html

Toshiki Shibahara | Social Informatics Laboratories | NTT R&D Website

interests include security of machine-learning-based systems, particularly attacks against deep neural

https://www.rd.ntt/e/sil/overview/evangelist/toshiki_shibahara.html

poster_en_5.pdf

method to stabilize training of Recurrent Neural Networks (RNNs). The RNN is one of the most successful

https://www.rd.ntt/cs/event/openhouse/2017/exhibition/5/poster_en_5.pdf

NTT Communication Science Laboratories Open House 2019

based on neural networks When children begin to understand hiragana Emergent literacy development in

https://www.rd.ntt/cs/event/openhouse/2019/program_en.html

NTT Communication Science Laboratories Open House 2020 Exhibition

recovery-command sequences by neural networks Refining spatially aggregated data from cities Multivariate

https://www.rd.ntt/cs/event/openhouse/2020/exhibition_en.html

NTT Communication Science Laboratories Open House 2020

resonance (CMR) imaging, by combining it with the deep neural networks-based anatomical segmentation of CMR

https://www.rd.ntt/cs/event/openhouse/2020/exhibition21/index_en.html

井田 安俊 | NTT R&D Website

Learning Rate via Covariance Matrix Based Preconditioning for Deep Neural Networks", International Joint

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

Dr.Naonori Ueda | NTT R&D Website

Chemical Research, 2003-September 2012. Associate Editor, Neural Networks Journal, 2003-2010. General Chair

https://www.rd.ntt/e/organization/researcher/fellow/f_003.html

PowerPoint Presentation

 Matrix Factorization  Random Walk  Deep Neural Networks The KDD’17 Tutorials  Learning Representations

https://www.rd.ntt/_assets/pdf/sic/event/2018/1/09_panel_jeffrey.pdf

スライド 1

combining it with the deep neural networks-based anatomical segmentation of CMR imaging. Masahiro Nakano

https://www.rd.ntt/cs/event/openhouse/2020/download/c_21_en.pdf

NTT コミュニケーション科学基礎研究所 オープンハウス2013 誰がどのように話しても正確に聞き取ります ～話者や発話スタイルの多様性に頑健な音声認識技術～

deep neural networks into structured classification approach based on weighted finite-state transducers

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

Microsoft PowerPoint - C_パネル一覧0501.pptx

facial expression improvement with multi-task deep neural networks,” in Proc. The 24th ACM International

https://www.rd.ntt/cs/event/openhouse/2017/exhibition/16/poster16.pdf

NTT Communication Science Laboratories Open House 2019

spoke when & what? How many people were there? - All-neural source separation, counting and diarization

https://www.rd.ntt/cs/event/openhouse/2019/exhibition17/index_en.html

Secure Computation AI | NTT R&D Website

maintain accuracy in calculations such as reciprocals and square roots, essential for neural networks

https://www.rd.ntt/e/research/SI0014.html

Visual Mechanisms for Perceiving Materials | NTT Communication Science Laboratories | NTT R&D Website

be computed by neural networks in the retina or in the low-level visual cortex. Changing Materials by

https://www.rd.ntt/e/cs/team_project/human/representation/research_human01.html

Recognition Research Group | NTT Communication Science Laboratories | NTT R&D Website

Visual Feedback Generation for Facial Expression Improvement with Multi-task Deep Neural Networks", The

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

Science and Technology Are the Collective Wisdom of Our Predecessors. It Is Our Mission-the Researchers of Today-to Make Them Even Better | NTT R&D Website

deep learning and neural networks, it is important to repeat the process of verifying a hypothesis

https://www.rd.ntt/e/research/JN202305_21819.html

Signal Processing Research Group | NTT Communication Science Laboratories | NTT R&D Website

Of Neural- And Clustering-Based Diarization Through Deep Unfolding Of Infinite Gaussian Mixture Model

https://www.rd.ntt/e/cs/team_project/media/signal/

NTT Communication Science Laboratories Open House 2017

recurrent units Abstract We propose a method to stabilize training of Recurrent Neural Networks (RNNs). The

https://www.rd.ntt/cs/event/openhouse/2017/exhibition/5/index_en.html

スライド 1

Face-to-voice conversion and voice-to-face conversion - Crossmodal voice conversion with deep

https://www.rd.ntt/cs/event/openhouse/2019/download/19_c_en.pdf

Unsupervised Learning of 3D Representations from 2D images | NTT Communication Science Laboratories | NTT R&D Website

overcome this limitation, we developed a deep generative model that uses a camera aperture rendering

https://www.rd.ntt/e/cs/team_project/media/recognition/research_media15.html

poster_en_13.pdf

networks / Deep learning [low memory requirement] able to provide WEVs 10 to 100 times smaller than those

https://www.rd.ntt/cs/event/openhouse/2017/exhibition/13/poster_en_13.pdf

上田 修功 | NTT R&D Website

. Associate Editor, Neural Networks Journal, 2003-2010. 電子情報通信学会 情報論的学習理論時限研究専門委員会, 専門委員長, 2003-2004年. 情報論的学習

https://www.rd.ntt/organization/researcher/fellow/f_003.html

信号処理研究グループ｜NTTコミュニケーション科学基礎研究所｜NTT R&D Website

Of Neural- And Clustering-Based Diarization Through Deep Unfolding Of Infinite Gaussian Mixture Model

https://www.rd.ntt/cs/team_project/media/signal/

Communication with Desired Voice | NTT R&D Website

input speech and target speech. 2. Research on speech × deep generative model A typical voice-conversion

https://www.rd.ntt/e/research/JN202009_6715.html

Photonic Implementation of Reservoir Computing | NTT R&D Website

artificial neural networks (ANNs). Their computation is based on a huge amount of matrix operations and

https://www.rd.ntt/e/research/JN202206_18595.html

Optical Circuit Technologies for Next-generation Computing Using Light | NTT R&D Website

the research introduced in this article, research on neural networks using optical circuits has been

https://www.rd.ntt/e/research/JN202206_18579.html

メディア認識研究グループ｜NTTコミュニケーション科学基礎研究所｜NTT R&D Website

N. Harada, "Phase reconstruction based on recurrent phase unwrapping with deep neural networks," in

https://www.rd.ntt/cs/team_project/media/recognition/

Report on the NTT R&D Forum — Road to IOWN 2022｜NTT R&D Website

. The AI boom is supported by deep learning technology. Its many layers of neural networks learn from a

https://www.rd.ntt/e/forum/2022/keynote_2.html