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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
IBISML 2015-03-05
16:15		 Kyoto Kyoto University Adaptation of Machine Learning Method for Music Structure Analysis
Yoshiyuki Kushibe, Toshiaki Takita (Univ. of Tsukuba), Masatoshi Hamanaka (Kyoto Univ.), Sakurako Yazawa, Junichi Hoshino (Univ. of Tsukuba) IBISML2014-89		 This paper describes the music structure analysis method using machine learning. Music structure analysis is to automati... [more] IBISML2014-89
pp.31-38
PRMU 2014-03-14
15:30		 Tokyo   Experimental study on effect of pre-training in deep learning through visualization of unit outputs
Tsubasa Ochiai (Doshisha Univ./NICT), Hideyuki Watanabe (NICT), Shigeru Katagiri, Miho Ohsaki (Doshisha Univ.), Shigeki Matsuda, Chiori Hori (NICT) PRMU2013-210		 To clarify the capability of recent powerful classifier concept, Deep Neural Networks (DNN), we experimentally
investig... [more]		 PRMU2013-210
pp.253-258
SP, IPSJ-SLP 2013-12-20
10:45		 Tokyo   [Invited Talk] Acoustic Modeling Using Restricted Boltzmann Machines and Deep Belief Networks for Statistical Parametric Speech Synthesis and Voice Conversion
Zhen-Hua Ling, Ling-Hui Chen, Li-Rong Dai (USTC) SP2013-90		 This paper summarizes our previous work on spectral modeling using restricted Boltzmann machines (RBM) and deep belief n... [more] SP2013-90
pp.103-108
 Results 1 - 4 of 4  /   
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