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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
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Committee Date Time Place Paper Title / Authors Abstract Paper #
RCS, SIP, IT 2022-01-21
15:00		 Online Online [Invited Talk] Deep Learning-Aided Belief Propagation for Large Multiuser MIMO Detection
Takumi Takahashi (Osaka Univ.), Shinsuke Ibi (Doshisha Univ.), Seiichi Sampei (Osaka Univ.) IT2021-80 SIP2021-88 RCS2021-248		 With the increasing dimensionality of wireless communication signals, low-complexity signal detection algorithms to solv... [more] IT2021-80 SIP2021-88 RCS2021-248
pp.289-294
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2021-06-28
13:00		 Online Online Nonparametric Bayesian Deep Visualization
Haruya Ishizuka (Bridgestone Corp.), Daichi Mochihashi (ISM) NC2021-1 IBISML2021-1		 (To be available after the conference date) [more] NC2021-1 IBISML2021-1
pp.1-8
PRMU, IPSJ-CVIM 2021-03-04
10:45		 Online Online [Short Paper] High-Resolution Image Completion by Hierarchical Neural Process
Masato Miyahara, Daisuke Sato, Masato Fukuda, Narimune Matsumura, Yoshiki Nishikawa (NTT) PRMU2020-74		 Neural Process (NP) is a deep generation model which can consider the uncertainty of prediction.
The unknown output is ... [more]		 PRMU2020-74
pp.31-34
SP 2020-01-29
11:30		 Toyama   Application of Deep Gaussian Process to Multi-Speaker Text-to-Speech Synthesis using Speaker Codes
Kentaro Mitsui, Tomoki Koriyama, Hiroshi Saruwatari (UTokyo) SP2019-49		 Speaker codes are widely used to achieve multi-speaker text-to-speech synthesis.
Conventionally, Deep Neural Network (D... [more]		 SP2019-49
pp.31-36
SIP, EA, SP, MI
(Joint) [detail]		 2018-03-19
10:50		 Okinawa   On the Use of Deep Gaussian Processes for GPR-based Speech Synthesis
Tomoki Koriyama, Takao Kobayashi (Tokyo Inst. of Tech.) EA2017-106 SIP2017-115 SP2017-89		 This paper proposes a speech synthesis framework
based on deep Gaussian processes (DGPs).
DGP is a Bayesian deep learn... [more]		 EA2017-106 SIP2017-115 SP2017-89
pp.27-32
 Results 1 - 5 of 5  /   
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