Result for "Deep Learning",210
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/
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/
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
Media Information Laboratory Past news | NTT Communication Science Laboratories | NTT R&D Website
Boeddeker, Tsubasa Ochiai, "Microphone Array Signal Processing and Deep Learning for Speech Enhancement
https://www.rd.ntt/e/cs/team_project/media/past_news.html
Theoretical Understanding of Source-free Domain Adaptation | NTT Communication Science Laboratories | NTT R&D Website
Laboratories Related Research Theoretical Understanding of Source-free Domain Adaptation Deep Image Generation
https://www.rd.ntt/e/cs/team_project/media/recognition/research_media20.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
Media Information Laboratory | NTT Communication Science Laboratories | NTT R&D Website
(2024). Microphone Array Signal Processing and Deep Learning for Speech Enhancement: Combining model
https://www.rd.ntt/e/cs/team_project/media/
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/
Efficient Algorithm for K-Multiple-Means | NTT Communication Science Laboratories | NTT R&D Website
Theoretical Understanding of Source-free Domain Adaptation Deep Image Generation Based on Optics and Physics
https://www.rd.ntt/e/cs/team_project/media/recognition/research_media24.html
poster_en_5.pdf
Japanese) Sekitoshi Kanai Software Innovation center Email：kanai.sekitoshi(at)lab.ntt.co.jp Stable deep
https://www.rd.ntt/cs/event/openhouse/2017/exhibition/5/poster_en_5.pdf
G03-02-e.pdf
difficult. By modeling both the metalens and image reconstruction using deep learning to jointly optimize
https://www.rd.ntt/forum/2024/doc/G03-02-e.pdf
c_20.pdf
. Harada, G. Zhang, B. Kleijn, “Edge-consensus learning: deep learning on P2P networks with nonhomogeneous
https://www.rd.ntt/cs/event/openhouse/2020/download/c_20.pdf
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
Processing and Deep Learning for Speech Enhancement: Combining model-based and data-driven approaches to
https://www.rd.ntt/cs/team_project/media/signal/
NTT Communication Science Laboratories Open House 2018
for language acquisition by infants. The remarkable progress of deep learning is representative of
https://www.rd.ntt/cs/event/openhouse/2018/talk/director/index_en.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
を自在に探し出して活用・操作可能にする仕組みの実現をめざします。 関連文献 O. Krishna, G. Irie, X. Wu, T. Kawanishi, K. Kashino, “Learning
https://www.rd.ntt/cs/event/openhouse/2020/exhibition19/
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
Kenji Iwana, and Seiichi Uchida, "Attention to warp: Deep metric learning for multivariate time series
https://www.rd.ntt/cs/team_project/media/recognition/
スライド 1
学習のボトルネック解消で精度を向上 Larger capacity output function for deep learning ～深層学習における、より高い表現能力を持つ出力関数～ 深層学習
https://www.rd.ntt/cs/event/openhouse/2019/download/04_a.pdf
c_19.pdf
.ntt.co.jp [1] O. Krishna, G. Irie, X. Wu, T. Kawanishi, K. Kashino, “Learning search path for region
https://www.rd.ntt/cs/event/openhouse/2020/download/c_19.pdf
スライド 1
on deep learning [1] K. Zmolikova, M. Delcroix, K. Kinoshita, T. Higuchi, A. Ogawa, T. Nakatani
https://www.rd.ntt/cs/event/openhouse/2018/exhibition/16/poster16.pdf