Result for "Deep Learning",188

poster_en.pdf

【Reference】【Co nt ac t】 Abstract Fast and Accurate Deep Learning Yasutoshi Ida Software Innovation

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

High-speed optical interconnect for AI : Distributed deep learning accelerator | NTT Device Technology Laboratories | NTT R&D Website

High-speed optical interconnect for AI : Distributed deep learning accelerator | NTT Device

https://www.rd.ntt/e/dtl/technology/pe_product-optical-interconnect.html

NTT Communication Science Laboratories Open House 2016

Fast and accurate deep learning - Efficient learning utilizing directions of past gradients - Abstract

https://www.rd.ntt/cs/event/openhouse/2016/exhibition/7/index_en.html

Sekitoshi Kanai | NTT Software Innovation Center | NTT R&D Website

Project Researcher (As of November 2018) Making the current deep learning with AI faster, easier and safer

https://www.rd.ntt/e/sic/team_researchers/researcher/249.html

スライド 1

Improving the accuracy of deep learning - Larger capacity output function for deep learning - Deep learning

https://www.rd.ntt/cs/event/openhouse/2019/download/04_a_en.pdf

NTT Communication Science Laboratories Open House 2019

Improving the accuracy of deep learning - Larger capacity output function for deep learning - Abstract Deep

https://www.rd.ntt/cs/event/openhouse/2019/exhibition4/index_en.html

スライド 1

for efficient object search We propose Adaptive Spotting, a deep reinforcement learning approach to

https://www.rd.ntt/cs/event/openhouse/2020/download/c_19_en.pdf

Network-AI技術 – 主要な外部発表

. Matsuo and K. Watanabe, "Fault Detection of ICT systems with Deep Learning Model for Missing Data, " 2021

https://www.rd.ntt/ns/qos/research/05/presentation.html

スライド 1

from their voices with deep learning model Applications Call center response decision, marketing

https://www.rd.ntt/cs/event/openhouse/2020/download/c_15_en.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

analysis by environmental sensing and machine learning Memory efficient deep learning for mobile devices

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

NTT Communication Science Laboratories Open House 2016

Thursday, June 2th 15:30 - 16:10 Avenues toward super-human speech recognizers - Advances in deep learning

https://www.rd.ntt/cs/event/openhouse/2016/talk/research1/index_en.html

NTT Communication Science Laboratories Open House 2017

Abstract The rapid progress of deep learning is impacting the world we live in. Media generation (e.g

https://www.rd.ntt/cs/event/openhouse/2017/talk/research2/index_en.html

スライド 1

modeling,” under review.[2] T. Ngo, Z. Lu, G. Carneiro,“Combining deep learning and level set for the

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

NTT Communication Science Laboratories Open House 2020

technology and our approaches with the theme "What can be done when the voice is combined with deep learning

https://www.rd.ntt/cs/event/openhouse/2020/research1/index_en.html

スライド 1

Signal Processing Research Group, Media Information Laboratory We propose an AI system (a deep learning

https://www.rd.ntt/cs/event/openhouse/2018/exhibition/17/poster_en_17.pdf

NTT Communication Science Laboratories Open House 2017 Program

) 11:00 - 11:40 Generative personal assistance with audio and visual examples Deep learning opens the

https://www.rd.ntt/cs/event/openhouse/2017/program_en.html

KOBAYASHI, Masahiro

, May 2018. A. Suzuki and M. Kobayashi, "Multi-Agent Deep Reinforcement Learning for Cooperative

https://www.rd.ntt/e/ns/qos/person/m_kobayashi/

田尻 兼悟

, Yasuhiro Ikeda, Yuusuke Nakano, Keishiro Watanabe“Dividing Deep Learning Model for Continuous Anomaly

https://www.rd.ntt/ns/qos/person/tajiri/

Automated Data Analysis "RakuDA" | NTT Software Innovation Center | NTT R&D Website

of distributed data processing platform SE project includes deep learning and machine learning. Our

https://www.rd.ntt/e/sic/team_researchers/team/250.html

NTT Communication Science Laboratories Open House 2020

Spotting, a deep reinforcement learning approach to object search from a scene represented by a 3D point

https://www.rd.ntt/cs/event/openhouse/2020/exhibition19/index_en.html

NTT Communication Science Laboratories Open House 2019

population data Improving the accuracy of deep learning Larger capacity output function for deep learning

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

NTT Communication Science Laboratories Open House 2017

Exhibition Program 16 Generative personal assistance Deep learning opens the way to innovative media

https://www.rd.ntt/cs/event/openhouse/2017/exhibition/16/index_en.html

poster_en_16.pdf

process automatically. To this end, we propose two kinds of learning-based (specifically, deep learning

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

スライド 1

comfort and energy saving ⇒ Maximize conflicting indicators by deep reinforcement learning to calculate

https://www.rd.ntt/cs/event/openhouse/2019/download/02_a_en.pdf

NTT Communication Science Laboratories Open House 2015

Extracting essential information from sounds - Advances in distant speech recognition by deep learning

https://www.rd.ntt/cs/event/openhouse/2015/exhibition/20/index_en.html

Generalized Domain Adaptation | NTT Communication Science Laboratories | NTT R&D Website

is well-known that deep-learning models degrade in performance when there are environmental

https://www.rd.ntt/e/cs/team_project/media/recognition/research_media16.html

NTT Communication Science Laboratories Open House 2020

, “Edge-consensus learning: deep learning on P2P networks with nonhomogeneous data,” submitted to KDD 2020

https://www.rd.ntt/cs/event/openhouse/2020/exhibition20/index_en.html

スライド 1

deep learning explains the tuning in presence of reverberation  The frequency tuning may be optimal

https://www.rd.ntt/cs/event/openhouse/2018/exhibition/24/poster_en_24.pdf

NTT Communication Science Laboratories Open House 2016 Program

deep learning and signal processing that are making speech recognition leap forward - Takuya Yoshioka

https://www.rd.ntt/cs/event/openhouse/2016/program_en.html

NTT Communication Science Laboratories Open House 2016

How did you get here? Where will you go? - Trajectory analysis and prediction using deep learning

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

Network-AI技術

: Deep Anomaly Surveillance」の技術解説 Technical description about Deep Learning Based Anomaly Detection

https://www.rd.ntt/ns/qos/research/05/

Photonic reservoir computing : Optics makes a machine learning much faster | NTT Device Technology Laboratories | NTT R&D Website

Preprocessing of large-scale video streaming to assist deep learning Related technologies Optical packet

https://www.rd.ntt/e/dtl/technology/pe_product-photonic-reservoir.html

MATSUO, Yoichi

researches on network management. Research Interests Network operation Machine Learning, Deep Learning

https://www.rd.ntt/e/ns/qos/person/matsuo/

NTT Communication Science Laboratories Open House 2018

hypothesis selection Abstract We propose an AI system (a deep learning model) that solves two-choice

https://www.rd.ntt/cs/event/openhouse/2018/exhibition/17/index_en.html

The Front Line of Data Security｜NTT R&D Website

Learning Processing in the Deep Learning of the Standard AI with Encryption As introduced in the first

https://www.rd.ntt/e/security/0002.html

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

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

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

鈴木 晃人

-Agent Deep Reinforcement Learning for Cooperative Computing Offloading and Route Optimization in Multi

https://www.rd.ntt/ns/qos/person/a_suzuki/

HASHIMOTO, Yuka

. Yuka Hashimoto, Masahiro Ikeda, and Hachem Kadri, Deep learning with kernels through RKHM and the

https://www.rd.ntt/e/ns/qos/person/hashimoto/

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

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

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

スライド 1

Computing Technology Project, Software Innovation Center 05 Memory efficient deep learning for mobile

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

E07_leaf_e.pdf

. Summary Our technology can quickly generate efficient routes using AI (deep reinforcement learning) while

https://www.rd.ntt/forum/2023/doc/E07_leaf_e.pdf

Video Library | Communication Traffic, Quality and Operation Research Project | NTT Network Service Systems Laboratories | NTT R&D Website

learning based anomaly detection technology - DeAnoS: Deep Anomaly Surveillance - Predictive routing

https://www.rd.ntt/e/ns/qos/video_library.html

NTT Communication Science Laboratories Open House 2020 Exhibition

spotting for efficient object search Deep learning without data aggregation from nodes Asynchronous

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

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 2020

physical laws for 3D cardiac modeling,” under review. T. Ngo, Z. Lu, G. Carneiro, “Combining deep learning

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

P11_leaf_e.pdf

computing resources without copying for LLM learning Remote computing with APN - Future of tsuzumis' deep

https://www.rd.ntt/forum/2023/doc/P11_leaf_e.pdf

NTT Communication Science Laboratories Open House 2019

Download Contact Home / Program / Exhibition Program Exhibition Program Science of Machine Learning 05

https://www.rd.ntt/cs/event/openhouse/2019/exhibition5/index_en.html

スライド 1

スライド 1 Abstract References Contact オープンハウス 2020 20 Deep learning without data aggregation from

https://www.rd.ntt/cs/event/openhouse/2020/download/c_20_en.pdf

Getting closer to humans with AI and understanding humans with brain science: AI with a deep understanding of people, capable of coexisting with us｜NTT R&D Website

to near and surpass specific human abilities through research into fields such as deep learning

https://www.rd.ntt/e/ai/0003.html

F12_leaf_e.pdf

Through understanding deep learning models, we enhance their fairness and safety Understanding the

https://www.rd.ntt/forum/2023/doc/F12_leaf_e.pdf

スライド 1

degradation. On the other hand, the proposed method, which is based purely on deep learning, can theoretically

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

スライド 1

listen to - Computational selective hearing based on deep learning - Use a few seconds of speech from the

https://www.rd.ntt/cs/event/openhouse/2018/exhibition/16/poster_en_16.pdf

橋本 悠香

for full-rank weights, ICLR 2024. Yuka Hashimoto, Masahiro Ikeda, and Hachem Kadri, Deep learning with

https://www.rd.ntt/ns/qos/person/hashimoto/

NTT Communication Science Laboratories Open House 2017

) representation, and a deep learning-based approach using the generative adversarial network (GAN). The former

https://www.rd.ntt/cs/event/openhouse/2017/exhibition/17/index_en.html

スライド 1

signals with similar voice characteristics  SpeakerBeam （= Selective Hearing based on Deep Learning） Deep

https://www.rd.ntt/cs/event/openhouse/2020/download/c_17_en.pdf

スライド 1

learning Our goal is to automatically discover “causal relationships” from time series data, i.e., a

https://www.rd.ntt/cs/event/openhouse/2019/download/05_a_en.pdf

poster_en.pdf

learning～[1] Y. Endo, H. Toda, K. Nishida, A. Kawanobe,“Deep feature extraction from trajectories for

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

『NTT R&Dフォーラム 2018』開催報告　～デジタル技術が彩る未来へ～｜NTT R&D Website

シーの数値を近隣にいるタクシーの位置情報とともにマッピングしたつばめグループのカーナビ Deep Learningを短時間で低コストに行います（corevoを支える基盤技術：E15） 昨今、さま

https://www.rd.ntt/forum/forum2018.html

NTT Communication Science Laboratories Open House 2018

Program 24 Understanding human hearing with AI Analyzing auditory neural mechanisms with machine learning

https://www.rd.ntt/cs/event/openhouse/2018/exhibition/24/index_en.html

NTT Communication Science Laboratories Open House 2017

Exhibition Program 5 Stable deep learning for time-series data Preventing gradient explosions in gated

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

Statistical Machine Learning | NTT Communication Science Laboratories | NTT R&D Website

in deep learning technology have made it possible to build high precision machine learning models for

https://www.rd.ntt/e/cs/team_project/icl/ls/research_innovative02.html

Dr.Naonori Ueda | NTT R&D Website

preparation) Mulia,I.E.,Ueda,N.,Miyoshi,T.,Iwamoto,T.& Heidarzadeh,M.A novel deep learning approach for

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

Secure Computation AI | NTT R&D Website

data. Advantages of this technology The world's first technology that enables standard deep learning

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

NTT Communication Science Laboratories Open House 2019

, thanks to recent AI developments especially in deep learning, computers are approaching?and surpassing in

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

Media Information Laboratory Past news | NTT Communication Science Laboratories | NTT R&D Website

event representation learning for semantic-level generation and editing of avatar motion” has been

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

2020_booklet_en.pdf

- Adaptive spotting for efficient object search 20. Deep learning without data aggregation from nodes

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

NTT Communication Science Laboratories Open House 2015 Program

- Advances in distant speech recognition by deep learning - Measurement of fluorescence by 9-eye camera

https://www.rd.ntt/cs/event/openhouse/2015/program_en.html

poster.pdf

がります。 0005 Trajectory analysis and prediction using deep learning 1.セグメント分割 2.特徴抽出 3.移動手段判定移動手段推定技術移動軌跡* 時刻

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

スライド 1

on Human Computation and Crowdsourcing （HCOMP2019）, 2019. Deep learning requires much labeled data

https://www.rd.ntt/cs/event/openhouse/2020/download/a_01_en.pdf

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

, Brian Kenji Iwana, and Seiichi Uchida, "Attention to warp: Deep metric learning for multivariate time

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

F14_leaf_e.pdf

on deep learning using space information collected in the verification environment We confirmed and

https://www.rd.ntt/forum/2023/doc/F14_leaf_e.pdf

NTT Communication Science Laboratories Open House 2014 Defeat reverberation: enemy of speech recognition - Advanced speech enhancement and recognition -

reduction and deep learning-based automatic speech recognition. Our proposed system greatly improves

https://www.rd.ntt/cs/event/openhouse/2014/exhibition/20/index_en.html

Wataru Matsuda | NTT Social Informatics Laboratories | NTT R&D Website

by Monitoring DLL Using Deep Learning, Journal of Information Processing 28, 1052-1064 W Matsuda, M

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

NTT コミュニケーション科学基礎研究所 オープンハウス2020

. Harada, G. Zhang, B. Kleijn, “Edge-consensus learning: deep learning on P2P networks with nonhomogeneous

https://www.rd.ntt/cs/event/openhouse/2020/exhibition20/

Recruit | NTT Software Innovation Center | NTT R&D Website

technologies, machine learning and deep learning algorithm, processing platform technologies for big data

https://www.rd.ntt/e/sic/recruit/

poster_en_17.pdf

, called the composite Line Spectral Pair (LSP) representation, and a deep learning-based approach using

https://www.rd.ntt/cs/event/openhouse/2017/exhibition/17/poster_en_17.pdf

NTT Communication Science Laboratories Open House 2020

” such as “media processing” and “data analysis and machine learning” and science for “obtaining a deep

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

Microsoft PowerPoint - C_パネル一覧0501.pptx

にはユーザのあらゆる要望に応えられるメディア生成技術の確立を目指しています。 16 Deep learning opens the way to innovative media generation 金子

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

NTT Communication Science Laboratories Open House 2016

and deep learning-based ASR technologies to address such issues, with which we won the CHiME-3

https://www.rd.ntt/cs/event/openhouse/2016/exhibition/19/index_en.html

Video Library | Service Innovation Laboratory Group | NTT R&D Website

"MediaGnosis" Technology for creating a personalized sound zone Piper : Machine learning pipeline for traffic

https://www.rd.ntt/e/svlab/video_library/

poster_en_19.pdf

deep learning with long term feature Selamat siang [1] T. Yoshioka, N. Ito, M. Delcroix, A. Ogawa, K

https://www.rd.ntt/cs/event/openhouse/2017/exhibition/19/poster_en_19.pdf

松田 亘｜NTT社会情報研究所｜NTT R&D Website

Fujimoto, T Mitsunaga, Detection of Malicious Tools by Monitoring DLL Using Deep Learning, Journal of

https://www.rd.ntt/sil/overview/evangelist/wataru_matsuda.html

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

signal processing with deep-learning techniques," IEEE Signal Processing Magazine, vol. 36, no. 6, pp

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

Photonics-Electronics Convergence Laboratory | NTT Device Technology Laboratories | NTT R&D Website

deep learning accelerator Back to Technology Main Page

https://www.rd.ntt/e/dtl/technology/photonics_electronics.html

NTT Communication Science Laboratories Open House 2018

based on deep learning Abstract In conversations, when several people speak at the same time, people

https://www.rd.ntt/cs/event/openhouse/2018/exhibition/16/index_en.html

NTT Communication Science Laboratories Open House 2017 Schedule

Generative personal assistance with audio and visual examples - Deep learning opens the way to innovative

https://www.rd.ntt/cs/event/openhouse/2017/schedule_en.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

IOWN PETs | NTT R&D Website

Secure federated learning enables training of AI and deep learning models using data that are difficult

https://www.rd.ntt/sil/project/iown-pec/

上田 修功 | NTT R&D Website

Journals) (in preparation) Mulia,I.E.,Ueda,N.,Miyoshi,T.,Iwamoto,T.& Heidarzadeh,M.A novel deep learning

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

NTT Communication Science Laboratories Open House 2019

proposed method, which is based purely on deep learning, can theoretically learn and deal with any

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

The information processing infrastructure｜NTT R&D Website

framework f... "Inference Cloud" service to make deep learning more accessible Related News

https://www.rd.ntt/e/infrastructure/

NTT Communication Science Laboratories Open House 2018

Language Exhibition Program 7 Interpreting deep learning from network structure Detecting communities in

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

NTT Communication Science Laboratories Open House 2016 Schedule

super-human speech recognizers - Advances in deep learning and signal processing that are making speech

https://www.rd.ntt/cs/event/openhouse/2016/schedule_en.html

Major issues and research trends in the evolution of artificial intelligence (AI)｜NTT R&D Website

the development of big data analysis, machine learning, and deep learning. Commercialization is

https://www.rd.ntt/e/ai/0001.html

スライド 1

Recognition Research Group, Media Information Laboratory 07 Interpreting deep learning from network structure

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

2019_booklet_english.pdf

estimation from spatiotemporal population data～ 04 Improving the accuracy of deep learning ～Larger capacity

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

poster_en_5.pdf

) Sekitoshi Kanai Software Innovation center Email：kanai.sekitoshi(at)lab.ntt.co.jp Stable deep learning for

https://www.rd.ntt/cs/event/openhouse/2017/exhibition/5/poster_en_5.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2020

,” under review. T. Ngo, Z. Lu, G. Carneiro, “Combining deep learning and level set for the automated

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