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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 10 of 10  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU, IBISML, IPSJ-CVIM 2024-03-03
15:00
Hiroshima Hiroshima Univ. Higashi-Hiroshima campus
(Primary: On-site, Secondary: Online)
Generalized Denoising Autoencoders with Tweedie's formula
Yuta Aishima (NAIST), Sho Sonoda (RIKEN), Noboru Isobe (Tokyo Univ.), Kazushi Ikeda (NAIST) IBISML2023-40
Denoising autoencoders learn the score of the data-generating distribution, i.e., $nabla log p(x)$. However, theoretical... [more] IBISML2023-40
pp.1-5
IBISML 2020-10-21
15:35
Online Online IBISML2020-24 The goal of this study is to understand the information processing mechanism in a deep neural network (DNN) as a curve $... [more] IBISML2020-24
p.42
QIT
(2nd)
2019-11-19
11:30
Tokyo Gakushuin University Fast quantum algorithm for data approximation by optimized random features
Hayata Yamasaki (UTokyo), Sathyawageeswar Subramanian (University of Cambridge), Sho Sonoda (Riken), Masato Koashi (UTokyo)
 [more]
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2018-06-13
15:25
Okinawa Okinawa Institute of Science and Technology Current Dipole Localization from EEG with Birth-Death Process
Keita Nakamura (Waseda Univ.), Sho Sonoda (RIKEN), Hideitsu Hino (ISM), Masahiro Kawasaki (Univ. of Tsukuba), Shotaro Akaho (AIST), Noboru Murata (Waseda Univ.) IBISML2018-10
We explore the EEG source localization problem as the estimation of current dipoles. We formulate the relation between c... [more] IBISML2018-10
pp.67-74
PRMU, IBISML, IPSJ-CVIM [detail] 2017-09-16
14:05
Tokyo   [Invited Talk] Transportation aspect of deep neural network
Sho Sonoda (Waseda Univ.) PRMU2017-60 IBISML2017-32
What is happening in deep neural networks? In this talk, we formulate them as transport maps. From the transportation vi... [more] PRMU2017-60 IBISML2017-32
pp.185-188
IBISML 2017-03-06
16:30
Tokyo Tokyo Institute of Technology New Lerning Algorythm of Neural Network using Integral Representation and Kernel Herding
Takuo Matsubara, Sho Sonoda, Noboru Murata (Waseda Univ.) IBISML2016-103
A new learning algorithm for neural networks that converges at $mathcal{O}(frac{1}{n})$ with respect to model complexity... [more] IBISML2016-103
pp.25-31
IBISML 2016-11-17
14:00
Kyoto Kyoto Univ. Transportation aspect of infinitely deep denoising autoencoder
Sho Sonoda, Noboru Murata (Waseda Univ.) IBISML2016-88
The hidden layers of a deep neural network extract feature of the input data. We propose to analyze the feature map of a... [more] IBISML2016-88
pp.297-304
IBISML 2015-11-26
15:00
Ibaraki Epochal Tsukuba [Poster Presentation] CNN Brake Scene Recognition with LiDAR
Tatsunosuke Shimada, Takuo Matsubara, Sho Sonoda, Noboru Murata (Waseda Univ.), Patricia Ortal, Shinpei Kato (Nagoya Univ.) IBISML2015-61
 [more] IBISML2015-61
pp.61-67
PRMU, IBISML, IPSJ-CVIM [detail] 2014-09-02
15:45
Ibaraki   Sampling Learning Algorithm by Oracle Distribution
Sho Sonoda, Noboru Murata (Waseda Univ.) PRMU2014-52 IBISML2014-33
A new sampling learning algorithm for neural networks is proposed. Based on the integral representation of neural networ... [more] PRMU2014-52 IBISML2014-33
pp.137-142
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2014-06-26
15:00
Okinawa Okinawa Institute of Science and Technology Current Dipole Localization from EEG by Multiple Particle Filters and Model Selection
Yuki Kaneda, Sho Sonoda (Waseda Univ.), Hideitsu Hino (Univ. of Tsukuba), Noboru Murata (Waseda Univ.) NC2014-5 IBISML2014-5
In this study, the location, the moment and the number of ionic current modeled as dipoles are estimated from EEG data. ... [more] NC2014-5 IBISML2014-5
pp.91-96
 Results 1 - 10 of 10  /   
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