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All Technical Committee Conferences  (All Years)

Search Results: Conference Papers
 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 32  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
AI 2019-07-22
15:55
Hokkaido   A Recommendation System using Auto-encoders for Data Divided on Item Densities
Go Tanioka, Akihiro Inokuchi (KGU)
In recent years, deep learning have attracted attention in the field of machine learning, and its applications to the re... [more] AI2019-14
pp.71-75
SeMI, RCS, NS, SR, RCC
(Joint)
2019-07-11
15:00
Osaka I-Site Nanba(Osaka) Deep learning-based classification for the automatic of eNodeB state management in LTE networks
Kazuki Hara (Tsukuba Univ.), Kohei Shiomoto (TCU), Chin Lam Eng, Sebastian Backstad (Ericsson Japan)
It is crucial to identify the cause immediately when a failure occurs at base station of mobile communication. However, ... [more] RCC2019-41 NS2019-77 RCS2019-134 SR2019-53 SeMI2019-50
pp.145-150(RCC), pp.171-176(NS), pp.167-172(RCS), pp.177-182(SR), pp.159-164(SeMI)
SIS, IPSJ-AVM, ITE-3DIT [detail] 2019-06-13
13:15
Nagasaki Fukue Culture Center Autoencoders having surplus neurons in a hidden layer
Akihiro Suzuki, Hakaru Tamukoh (KYUTECH)
Unknown data is not compatible with a supervised training. This study employ autoencoders (AEs) to detect unknwon data. ... [more] SIS2019-3
pp.11-16
SIS 2019-03-06
14:30
Tokyo Tokyo Univ. Science, Katsushika Campus Complementary Color Reconstruction by Autoencoders
Akihiro Suzuki, Hakaru Tamukoh (Kyutech)
This study proposes a novel training method for autoencoders (AEs) that gives the AEs complementary color images as targ... [more] SIS2018-41
pp.23-28
NC, MBE
(Joint)
2019-03-04
17:00
Tokyo University of Electro Communications Towards understandable deep learning in stacked autoencoders
Masumi Ishikawa (Kyutech)
Recent progress of deep learning(DP) is remarkable and its recognition ability is said to surpass that of humans. The ac... [more] NC2018-62
pp.99-104
IN, NS
(Joint)
2019-03-05
15:20
Okinawa Okinawa Convention Center Unified Driving Skill Analysis of Curves with Different Radii and Interior Angles of Automobiles based on Deep Learning
Takuya Kagawa, Naiwala P. Chandrasiri (Kogakuin Univ)
With the advancement of automobile technology and consumer motivation to buy automobiles, it is expected that self drivi... [more] IN2018-151
pp.403-408
ITS, IE, ITE-MMS, ITE-HI, ITE-ME, ITE-AIT [detail] 2019-02-19
16:00
Hokkaido Hokkaido Univ. A Study on Vectorizing Diet Preferences based on Large Foodlogging Data and DNN model
Yuji Goda (UTokyo), Sosuke Amano (foo.log Inc.), Yoko Yamakata, Kiyoharu Aizawa (UTokyo)
In usual dietary management, dieticians use the food logs to see one’s wellness. However, this method is not handy for o... [more]
MI 2019-01-23
14:00
Okinawa   Unsupervised Shadow Detection for Ultrasound Images by Deep Learning
Suguru Yasutomi (FLL), Akira Sakai (FATEC), Masaaki Komatsu (Riken), Ryu Matsuoka, Reina Komatsu, Tatsuya Arakaki, Mayumi Tokunaka (Showa-U), Hidenori Machino, Kazuma Kobayashi (NCC), Ken Asada (Riken), Syuzo Kaneko (NCC), Akihiko Sekizawa (Showa-U), Ryuji Hamamoto (Riken)
Medical ultrasound is widely used for diagnosing internal organs since it is non-invasive. Shadows are often appear in u... [more] MI2018-96
pp.151-156
CAS, NLP 2018-10-19
09:55
Miyagi Tohoku Univ. Price Prediction of Used Cars at Auto Auction by Deep Learning
Hiromichi Sakurai, Daiki Kudo (Ibaraki Univ.), Eriko Hasegawa, Rikozou Shimoyama, Ryosuke Fukunishi, Hiroki Mayuzumi (PROTO), Tomoya Suzuki (Ibaraki Univ.)
Dealers of used cars have to predict future auction prices when buying used cars.
However, it is very difficult to pred... [more]
CAS2018-50 NLP2018-85
pp.69-74
IEE-CMN, EMM, LOIS, IE, ITE-ME [detail] 2018-09-27
15:15
Oita Beppu Int'l Convention Ctr. aka B-CON Plaza [Special Talk] Coded Acquistion of Light Fields -- From Basis Representation to Deep Learning --
Keita Takahashi (Nagoya Univ.)
A light field, which is often understood as a set of dense multi-view images, has been utilized in various 2D/3D applica... [more] LOIS2018-15 IE2018-35 EMM2018-54
pp.29-30
MI 2018-07-24
14:35
Iwate aiina (Morioka, Iwate) Estimation of postmortem time for Ai-CT images by using Deep Learning
Shota Chai, Yasushi Hirano, Shoji Kido (Yamaguchi Univ.), Kazuyuki Kinoshita, Kunihiro Inai, Sakon Noriki (Univ. of Fukui)
Although estimation of postmortem time is important for criminal investigation or sudden in-hospital death in the middle... [more] MI2018-28
pp.33-37
SIP, EA, SP, MI
(Joint) [detail]
2018-03-20
13:55
Okinawa   DNN prefiltering for enhancement of voice recognition in noise environment
Jun Takahashi, Kentaro Murase (Fujitsu Labs.)
In this paper, we applied convolutional denoising autoencoder (CDAE) as the prefilter of voice recognition and evaluated... [more] EA2017-170 SIP2017-179 SP2017-153
pp.373-378
PRMU, BioX 2018-03-18
16:10
Tokyo   Toward image inbetweening using Latent Model
Paulino Cristovao (Univ. of Tsukuba), Yusuke Tanimura, Hidemoto Nakada, Hideki Asoh (AIST)
Image interpolation is a well known problem in computer vision. Many approaches are restricted to optical flow and convo... [more] BioX2017-49 PRMU2017-185
pp.79-84
PRMU, BioX 2018-03-18
17:10
Tokyo   A Style Transfer Method using Variational Autoencoder
Hidemoto Nakada, Hideki Asoh (AIST)
Image Style Transfer is a technique to render arbitrary image content with
arbitrary image 'style'.
Most existing... [more]
BioX2017-56 PRMU2017-192
pp.121-126
IN 2018-01-23
11:15
Aichi WINC AICHI Retraining anomaly detection model using Autoencoder
Yasuhiro Ikeda, Keisuke Ishibashi, Yusuke Nakano, Keishiro Watanabe, Ryoichi Kawahara (NTT)
An autoencoder has been attracting much attention as an anomaly detection algorithm.
The autoencoder enables unsupervis... [more]
IN2017-84
pp.77-82
SIS, IPSJ-AVM 2017-10-12
15:20
Nara Todaiji Culture Center An autoencoder reversing abnormal inputs
Akihiro Suzuki, Hakaru Tamukoh (Kyushu Inst. of Tech.)
Usages of autoencoders (AEs) are not only a dimension reducer, but a generative model using reconstruction. AEs are used... [more] SIS2017-26
pp.29-34
PRMU, IBISML, IPSJ-CVIM [detail] 2017-09-16
14:05
Tokyo   [Invited Talk] Transportation aspect of deep neural network
Sho Sonoda (Waseda Univ.)
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
ICTSSL 2017-07-14
10:00
Kagawa Kagawa Univ. Analysis of Driving Skills in Curve Scenes based on Deep Learning using Stacked Autoencoders
Takuya Kagawa, Naiwala P. Chandrasiri (Kogakuin Univ.)
With the advancement of automobile technology and consumer motivation to buy automobiles, it is expected that automatic ... [more] ICTSSL2017-24
pp.39-43
IN 2017-06-16
11:30
Fukushima Roudou-Fukushi-Kaikan (Koriyama) Inferring causal parameters of anomalies detected by autoencoder using sparse optimization
Yasuhiro Ikeda, Keisuke Ishibashi, Yusuke Nakano, Keishiro Watanabe, Ryoichi Kawahara (NTT)
The anomaly detection algorithm based on an autoencoder has attracted much attention.
An autoencoder is a neural networ... [more]
IN2017-18
pp.61-66
NLP 2017-05-12
11:00
Okayama Okayama University of Science Investigation of Fast Construction for Intrusion Detection System using Multi-Layer Extreme Learning Machine.
Daichi Noguchi, Masaharu Adachi (Tokyo Denki Univ.)
Recently, there are incremental threats of cyber security for holding the Olympic Games in Tokyo in 2020. The fast const... [more] NLP2017-18
pp.87-92
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