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Technical Committee on Information-Based Induction Sciences and Machine Learning (IBISML) (Searched in: 2021)
Search Results: Keywords 'from:2022-03-08 to:2022-03-08'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2022-03-08 10:00 |
Online | Online | Estimating average causal effect of intervention in continuous variables using machine learning Yoshiaki Kitazawa (MSI)  IBISML2021-30 |
The most widely discussed methods for estimating the Average Causal Effect / Average Treatment Effect are those for inte... [more] | IBISML2021-30 pp.1-7 |
| IBISML | 2022-03-08 10:25 |
Online | Online | Robust computation of optimal transport by β-potential regularization Shintaro Nakamura (Univ. Tokyo), Han Bao (Univ.Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. Tokyo) IBISML2021-31 |
Optimal transport (OT) has become a widely used tool to measure the discrepancy between probability distributions in th... [more] |
IBISML2021-31 pp.8-14 |
| IBISML | 2022-03-08 10:55 |
Online | Online | Real log canonical threshold of reduced rank regression when inputs are on a low dimensional hyperplane Joe Hirose, Sumio Watanabe (Tokyo Tech) IBISML2021-32 |
A reduced rank regression is a statistical model which estimates a linear regression function from in- puts to outputs w... [more] | IBISML2021-32 pp.15-18 |
| IBISML | 2022-03-08 11:20 |
Online | Online | Tree-Structured Generative Model with Latent Variables and Approximate Variational Bayesian Inference Naoki Ichijo, Yuta Nakahara (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IBISML2021-33 |
[more] | IBISML2021-33 pp.19-26 |
| IBISML | 2022-03-08 13:05 |
Online | Online | [Invited Talk]
--- Takashi Matsubara (Osaka Univ.) IBISML2021-34 |
Deep learning is being considered as the most promising approach to building an artificial intelligence (AI) system; it ... [more] | IBISML2021-34 p.27 |
| IBISML | 2022-03-08 13:45 |
Online | Online | [Invited Talk]
--- Saku Sugawara (NII) IBISML2021-35 |
(To be available after the conference date) [more] | IBISML2021-35 p.28 |
| IBISML | 2022-03-08 14:25 |
Online | Online | [Invited Talk]
--- Yuichi Yoshida (NII) IBISML2021-36 |
(To be available after the conference date) [more] | IBISML2021-36 p.29 |
| IBISML | 2022-03-08 15:15 |
Online | Online | [Invited Talk]
Distributed AI for Dynamic and Diverse Environments Shinya Takamaeda (Tokyo Univ.) IBISML2021-37 |
[more] | IBISML2021-37 p.30 |
| IBISML | 2022-03-08 15:55 |
Online | Online | [Invited Talk]
--- Masaaki Imaizumi (Tokyo Univ.) IBISML2021-38 |
(To be available after the conference date) [more] | IBISML2021-38 p.31 |
| IBISML | 2022-03-09 09:05 |
Online | Online | [Invited Talk]
--- Koji Fukagata (Keio Univ.) IBISML2021-39 |
In recent years, the application of machine learning to various problems of fluid mechanics has been actively studied. I... [more] | IBISML2021-39 p.32 |
| IBISML | 2022-03-09 09:40 |
Online | Online | [Invited Talk]
--- Satoru Tokuda (Kyushu Univ.) IBISML2021-40 |
Plasma is the fourth state of matter, in which individual electrons and ions move around at various speeds. The velocity... [more] | IBISML2021-40 p.33 |
| IBISML | 2022-03-09 10:15 |
Online | Online | [Invited Talk]
--- Takahiro Tsukahara (Tokyo University of Science) IBISML2021-41 |
Turbulence of viscoelastic fluids, such as dilute polymer/surfactant solutions, is of practical importance, because it c... [more] | IBISML2021-41 p.34 |
| IBISML | 2022-03-09 11:00 |
Online | Online | [Invited Talk]
--- Yoshinobu Kawahara (Kyushu Univ.) IBISML2021-42 |
[more] | IBISML2021-42 pp.35-36 |
| IBISML | 2022-03-09 11:35 |
Online | Online | [Invited Talk]
--- Masanobu Horie (RICOS Co. Ltd.) IBISML2021-43 |
Learning flow phenomena is an important problem for both theoretical and practical aspects. Graph neural networks are a ... [more] | IBISML2021-43 p.37 |
| IBISML | 2022-03-09 12:10 |
Online | Online | [Invited Talk]
--- Susumu Goto (Osaka Univ.) |
[more] | |
| IBISML | 2022-03-09 13:30 |
Online | Online | Is the Performance of My Deep Network Too Good to Be True?
-- A Direct Approach to Estimating the Bayes Error in Binary Classification -- Takashi Ishida (UTokyo), Ikko Yamane (Université Paris Dauphine-PSL/RIKEN), Nontawat Charoenphakdee (UTokyo), Gang Niu (RIKEN), Masashi Sugiyama (RIKEN/UTokyo) IBISML2021-44 |
[more] | IBISML2021-44 pp.38-45 |
| IBISML | 2022-03-09 13:55 |
Online | Online | Learning coefficients of deep linear neural networks Ryuji Ito, Miki Aoyagi, Seikai Ashizawa, Yuki Tsukamoto (Nihon Univ.), Daisuke Kaji (Denso) IBISML2021-45 |
Recently, the learning theory has been analyzed based on resolution of learning machine singularities in algebraic geome... [more] | IBISML2021-45 pp.46-52 |
| IBISML | 2022-03-09 14:20 |
Online | Online | IBISML2021-46 |
(To be available after the conference date) [more] | IBISML2021-46 pp.53-60 |
| IBISML | 2022-03-09 14:55 |
Online | Online | Infinite SCAN: Joint Estimation of Changes and the Number of Word Senses with Gaussian Markov Random Fields Seiichi Inoue, Mamoru Komachi (TMU), Toshinobu Ogiso (NINJAL), Hiroya Takamura (AIST), Daichi Mochihashi (ISM) IBISML2021-47 |
In this study, we propose a hierarchical Bayesian model that can automatically estimate the number of senses for each wo... [more] | IBISML2021-47 pp.61-68 |
| IBISML | 2022-03-09 15:20 |
Online | Online | Classification of Hematoma Markers Tsuyoshi Okita, Hokuto Hirano, Kanta Moriyama (Kyutech), Koichi Arimura, Nobutaka Mukae (Kyudai), Koji Iihara (Kokujyun) IBISML2021-48 |
[more] | IBISML2021-48 pp.69-74 |
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