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Technical Committee on Information-Based Induction Sciences and Machine Learning (IBISML) (Searched in: 2016)
Search Results: Keywords 'from:2016-11-16 to:2016-11-16'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Structural Change Detection in Lithography Systems Yosuke Otsubo (NIKON), Masashi Sugiyama (RIKEN/UTokyo)  IBISML2016-45 |
A lithography system, which consists of various mechanical units such as high-precision optical systems, prints circuit ... [more] | IBISML2016-45 pp.1-8 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Policy search based on sample clustering with Gaussian mixture model Taiki Yano, Shinichi Maeda (Kyoto Univ.) IBISML2016-46 |
EM-based Policy Hyper Parameter Exploration (EPHE)(Wang et al., 2016) is a method that kills two birds with one stone; ... [more] | IBISML2016-46 pp.9-15 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | [Poster Presentation]
Principal Component Analysis based unsupervised Feature Extraction applied to Bioinformatics Y-h. Taguchi (Chuo Univ.) IBISML2016-47 |
Recently, numerous researches were performed for the machine/statisitical learning. Among those, deep learning is especi... [more] | IBISML2016-47 pp.17-24 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Success or Failure of Compressed Sensing Judged from Cross Validation Yoshinori Nakanishi-Ohno, Koji Hukushima (UTokyo) IBISML2016-48 |
[more] | IBISML2016-48 pp.25-32 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | [Poster Presentation]
A Research for Efficient Equivalence Structure Search by Local Distribution of Variables Asahi Ushio (Keio Univ.), Yoshinobu Takahashi (University of Electro-Communications), Seiya Satoh (AIST), Hiroshi Yamakawa (Dwango AI Lab) IBISML2016-49 |
[more] | IBISML2016-49 pp.33-36 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Robust supervised learning under uncertainty in dataset shift Weihua Hu, Issei Sato (UTokyo), Masashi Sugiyama (RIKEN/UTokyo) IBISML2016-50 |
When machine learning is deployed in the real world, its performance can be significantly undermined because test data m... [more] | IBISML2016-50 pp.37-44 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | [Poster Presentation]
Musical Instrument Sound Classification through CNN with Wavelet Analysis Shu Eguchi, Masaru Tanaka, Jun Fujiki (Fukuoka Univ.), Takio Kurita (Hiroshima Univ.) IBISML2016-51 |
In music information processing, the instrument sounds analysis using a computer is an important research theme to analy... [more] | IBISML2016-51 pp.45-49 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | A generation model of representations of sentence by convolution neural network for machine translation Shin Chadani, Satoshi Yamane, Kouhei Sakurai (Kanazawa Univ.) IBISML2016-52 |
In the task of machine translation, the word order of the language for translation is one of the important elements. Eng... [more] | IBISML2016-52 pp.51-54 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | [Poster Presentation]
Normal distributions and Heisenberg group
-- From the point of view of information geometry -- Akira Tokimatsu, Masaru Tanaka (Fukuoka Univ.) IBISML2016-53 |
[more] | IBISML2016-53 pp.55-57 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Online heterogeneous mixture machine learning Tetsuya Ikehara, Satoshi Yamane (Kanazawa Univ.) IBISML2016-54 |
In recent years, utilization of big data has attracted the attention, and many techniques about data analysis have been ... [more] | IBISML2016-54 pp.59-64 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Proximal Average Accelerated Proximal Gradient Algorithm with Adaptive Restart Yoshihiro Nakazato, Kazuto Fukuchi, Jun Sakuma (Univ. Tsukuba) IBISML2016-55 |
When using multiple regularizers, their proximal mapping is not easily available in closed form. The method to calculat... [more] |
IBISML2016-55 pp.65-71 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Additive Model Decomposition with Global Sparse Structure for Multi-task Granger Causal Estimation Hitoshi Abe, Jun Sakuma (Univ. Tsukuba) IBISML2016-56 |
Causality estimation is one of the key issues in time-series data analysis. Granger causality is widely known as a form... [more] |
IBISML2016-56 pp.73-79 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | A study of message propagation algorithms for approximate MAP inference of large scale probabilistic models Takashi Sano, Yuuji Ichisugi (AIST AIRC) IBISML2016-57 |
(To be available after the conference date) [more] | IBISML2016-57 pp.81-86 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | [Poster Presentation]
An ensemble learning for MR image reconstruction Yufu Kasahara, Masato Inoue (Waseda Univ), Kaori Togashi (Kyoto Univ) IBISML2016-58 |
In order to shorten the magnetic resonance (MR) imaging time, a lot of image reconstruction methods from a small number ... [more] | IBISML2016-58 pp.87-91 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Selective Inference for High-Dimensional Binary Classification Yuta Umezu, Kazuya Nakagawa (NIT), Koji Tsuda (Univ. of Tokyo), Ichiro Takeuchi (NIT) IBISML2016-59 |
In machine learning and other related area, the number of features is often reduced by some feature selection procedure ... [more] | IBISML2016-59 pp.93-100 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Statistical Mechanical Analysis of Fast Online Learning with Weight Normalization Yuki Yoshida, Ryo Karakida, Masato Okada (UTokyo), Shun-ichi Amari (RIKEN) IBISML2016-60 |
Weight normalization (WN), a newly developed optimization algorithm for neural networks by Salimans & Kingma(2016), fact... [more] | IBISML2016-60 pp.101-108 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Inference of Classical Spin Model by Multidimensional Multiple Histogram Method Hikaru Takenaka (UTokyo), Kenji Nagata (UTokyo/AIST/JST), Takashi Mizokawa (Waseda Univ.), Masato Okada (UTokyo/RIKEN) IBISML2016-61 |
We propose a novel method for effective Bayesian inference of classical spin model by the multidimensional multiple hist... [more] | IBISML2016-61 pp.109-116 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | [Poster Presentation]
Approximate Method of Variational Bayesian Matrix Completion with Sparse Prior Ryota Kawasumi, Koujin Takeda (Ibaraki Univ) IBISML2016-62 |
[more] | IBISML2016-62 pp.117-121 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Policy Search with High-dimensional Context Variables Voot Tangkaratt (The Univ. of Tokyo), Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters (Technical Univ. of Darmstadt), Masashi Sugiyama (The Univ. of Tokyo) IBISML2016-63 |
[more] | IBISML2016-63 pp.123-130 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Efficient learning of ranking model using belief propagation Arise Kuriya, Toshiyuki Tanaka (Kyoto Univ.) IBISML2016-64 |
In the area of Learning to Rank, the models whose output is ranking are trained from data. The exponential model propos... [more] |
IBISML2016-64 pp.131-135 |
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