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Technical Committee on Information-Based Induction Sciences and Machine Learning (IBISML) (Searched in: 2018)
Search Results: Keywords 'from:2018-11-05 to:2018-11-05'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Shin-ichiro Goto (Kyoto Univ.), Hideitsu Hino (ISM)  IBISML2018-44 |
(Advance abstract in Japanese is available) [more] | IBISML2018-44 pp.1-8 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Parameter Density Inheritance Using Kernel Density Estimation for Efficient CNN Learning Keisuke Horiuchi, Keisuke Kameyama (Tsukuba Univ.) IBISML2018-45 |
CNNs have shown state-of-the-art performances on a large variety of classification tasks. However, because of its comple... [more] | IBISML2018-45 pp.9-15 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
A Fast Approximation of the Nadaraya-Watson Regression with the k-Nearest Neighbor Crossover Kernel Toshio Ito, Naoki Hamada, Kotaro Ohori, Hiroyuki Higuchi (FUJITSU LAB) IBISML2018-46 |
For a system with inputs and outputs, a nonparametric regression has been proposed to clarify the relationship between i... [more] | IBISML2018-46 pp.17-21 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Posterior mean approximation solution combining multiple image prior distributions in MR image reconstruction Nanako Kubota, Ken Harada (Waseda Univ.), Koji Fujimoto, Tomohisa Okada (Kyoto Univ.), Masato Inoue (Waseda Univ.) IBISML2018-47 |
In the MR image reconstruction, combining multiple image prior distributions is preferred to obtain better results, but ... [more] | IBISML2018-47 pp.23-28 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Inference for Logitistic Regression Mixture Model with Local Variational Approximation and Study for Variational Free Energy Fumito Nakamura, Ryosuke Konishi (Generic Solution), Yasushi Kiyoki (Keio) IBISML2018-48 |
A logistic regression mixture model (LRMM) is a mixed model of the Logistic regression model, and it is widely used in t... [more] | IBISML2018-48 pp.29-36 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | IBISML2018-49 |
(To be available after the conference date) [more] | IBISML2018-49 pp.37-44 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Generalized Dirichlet-Process-Means with f-Mean and Analysis of Influence Function Masahiro Kobayashi, Kazuho Watanabe (Toyohashi Tech.) IBISML2018-50 |
DP-means clustering was obtained as an extension of $K$-means clustering. While it is implemented with a simple and effi... [more] | IBISML2018-50 pp.45-52 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Variational Approximation Accuracy in Non-negative Matrix Factorization Naoki Hayashi (MSI) IBISML2018-51 |
The asymptotic behavior of the variational free energy of the non-negative matrix factorization (NMF) has been elucidate... [more] | IBISML2018-51 pp.53-60 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Safe Screening of a Sparse KLIEP Model on Markov Network Structural Change Detection Hiroki Sato (Gifu Univ), Motoki Shiga (Gifu Univ./JST/RIKEN), Makoto Yamada (Kyoto Univ./JST/RIKEN) IBISML2018-52 |
On gene network analysis, detecting network structural changes among different cell states is necessary to understand bi... [more] | IBISML2018-52 pp.61-68 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Learning Hierarchical Structure via Nearest Neighbor Search and Formal Concept Analysis Yuka Yoneda (Osaka Univ.), Mahito Sugiyama (NII), Takashi Washio (Osaka Univ.) IBISML2018-53 |
We propose to learn a hierarchical structure of clusters from continuous data via two-step procedure: We first binariz... [more] |
IBISML2018-53 pp.69-73 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Statistical Mechanical Analysis of Single-layer Perceptron with Layer Normalization Shiro Takagi, Yuki Yoshida, Masato Okada (Tokyo Univ.) IBISML2018-54 |
[more] | IBISML2018-54 pp.75-81 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Development of the topology-preserving image alignment based on a local deformation of kernel density function Masataka Watajima (KIT), Sayuri Kuge, Takeshi Ishihara (KU), Yuichi Iino (UT), Ryo Yoshida (ISM), Terumasa Tokunaga (KIT) IBISML2018-55 |
Image alignment or image registration is a process for aligning two or more images into a common coordinate system. Many... [more] | IBISML2018-55 pp.83-90 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
CNN's Robustness Against Large Scale Dataset Ryuichiro Hataya, Hideki Nakayama (UTokyo) IBISML2018-56 |
(To be available after the conference date) [more] | IBISML2018-56 pp.91-98 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Crowd Navigation via Bayesian Optimization of Multi-agent Simulation Hitoshi Shimizu, Takuma Otsuka, Tomoharu Iwata, Hiroshi Sawada, Futoshi Naya, Naonori Ueda (NTT) IBISML2018-57 |
In a large event where a large number of people gather, to prepare plans for safely guiding visitors in advance, predict... [more] | IBISML2018-57 pp.99-104 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Learning Restricted Boltzmann Machine via the Adaptive Thouless-Anderson-Palmer Mean-Field Approximation Chako Takahashi (Tohoku Univ.), Muneki Yasuda (Yamagata Univ.), Kazuyuki Tanaka (Tohoku Univ.) IBISML2018-58 |
(Advance abstract in Japanese is available) [more] | IBISML2018-58 pp.105-110 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Bayesian Optimization for Multiobjective Optimization and Multipoint Search Takashi Wada (KSL), Hideitsu Hino (ISM) IBISML2018-59 |
(To be available after the conference date) [more] | IBISML2018-59 pp.111-118 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
How does the complexity of critic affect the performance of WGAN? Akihiro Iohara, Toshiyuki Tanaka (Kyoto Univ.) IBISML2018-60 |
WGAN is a generative model that learns by minimizing the Wasserstein distance between the generator distribution and the... [more] | IBISML2018-60 pp.119-125 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Online Prediction for Varying Bernoulli Processes by Minimax Strategy Kenta Konagayoshi, Kazuho Watanabe (Toyohashi Tech) IBISML2018-61 |
Online prediction methods sequentially predict furure data from time series data. Since online pre- diction does not ne... [more] |
IBISML2018-61 pp.127-134 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Proposal of Hyperparameter Optimization Framework Using a Non-Stationary Multi-Armed Bandit Algorithm Kenshi Abe, Masahiro Nomura (CA) IBISML2018-62 |
Hyperparameter optimization problem is an important problem that appears in areas such as machine learning. Hyperparamet... [more] | IBISML2018-62 pp.135-142 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Watermarking of Neural Network with Exponential Weighting Parameters Ryota Namba (Tsukuba Univ.), Jun Sakuma (Tsukuba Univ./riken) IBISML2018-63 |
Deep learning has been achieving top performance in many tasks. Since training of a deep learning model requires a gr... [more] |
IBISML2018-63 pp.143-150 |
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