Result for "Deep Learning",188

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