| IEICE Technical Committee Submission System Conference Schedule |
Online Proceedings [Sign in] Tech. Rep. Archives |
Technical Committee on Information-Based Induction Sciences and Machine Learning (IBISML) (Searched in: 2020)
Search Results: Keywords 'from:2021-03-02 to:2021-03-02'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2021-03-02 10:00 |
Online | Online | Learning from Noisy Complementary Labels with Robust Loss Functions Hiroki Ishiguro (UTokyo), Takashi Ishida (UTokyo/RIKEN), Masashi Sugiyama (RIKEN/UTokyo)  IBISML2020-34 |
It has been demonstrated that large-scale labeled datasets facilitate the success of machine learning. However, collecti... [more] | IBISML2020-34 pp.1-8 |
| IBISML | 2021-03-02 10:25 |
Online | Online | Selective Inference for Convex Clustering Using Parametric Programming Yumehiro Omori, Yu Inatsu (Nitech), Ichiro Takeuchi (Nitech/RIKEN) IBISML2020-35 |
Traditional statistical inference assumes that the hypothesis is predetermined and cannot be used as is for statistical ... [more] | IBISML2020-35 pp.9-15 |
| IBISML | 2021-03-02 10:50 |
Online | Online | Kernel tensor decomposition based unsupervised feature extraction
-- Applications to bioinformatics -- Y-h. Taguchi (Chuo Univ.) IBISML2020-36 |
A lot of research has been done on the so-called textit{large p small n} problem, where the number of samples is small c... [more] | IBISML2020-36 pp.16-23 |
| IBISML | 2021-03-02 11:15 |
Online | Online | Interdisciplinary Integration by Artificial Intelligence
-- Tasks of Discipline Science -- Kumon Tokumaru (Writer) IBISML2020-37 |
It is time to integrate interdisciplinary sciences to develop Collective Human Intelligence. Research results of discipl... [more] | IBISML2020-37 pp.24-29 |
| IBISML | 2021-03-02 13:05 |
Online | Online | IBISML2020-38 |
(To be available after the conference date) [more] | IBISML2020-38 p.30 |
| IBISML | 2021-03-02 13:45 |
Online | Online | IBISML2020-39 |
(To be available after the conference date) [more] | IBISML2020-39 p.31 |
| IBISML | 2021-03-02 14:25 |
Online | Online | IBISML2020-40 |
[more] | IBISML2020-40 p.32 |
| IBISML | 2021-03-02 15:20 |
Online | Online | IBISML2020-41 |
[more] | IBISML2020-41 p.33 |
| IBISML | 2021-03-02 16:00 |
Online | Online | IBISML2020-42 |
[more] | IBISML2020-42 p.34 |
| IBISML | 2021-03-03 09:05 |
Online | Online | IBISML2020-43 |
[more] | IBISML2020-43 p.35 |
| IBISML | 2021-03-03 09:45 |
Online | Online | IBISML2020-44 |
Industrial applications of AI and machine learning technology are expanding. NTT DOCOMO drive an data-driven innovation... [more] |
IBISML2020-44 p.36 |
| IBISML | 2021-03-03 10:25 |
Online | Online | IBISML2020-45 |
In the US, the Holding Foreign Companies Accountable Act was passed on December 20, 2020. This act requires foreign comp... [more] | IBISML2020-45 p.37 |
| IBISML | 2021-03-03 11:15 |
Online | Online | IBISML2020-46 |
Developing a profitable trading strategy is a central problem in the financial industry. In this presentation, we develo... [more] | IBISML2020-46 p.38 |
| IBISML | 2021-03-03 11:55 |
Online | Online | IBISML2020-47 |
Mobility Technologies is providing a next-generation AI dashboard camera service called “DRIVE CHART” to help reduce tra... [more] | IBISML2020-47 p.39 |
| IBISML | 2021-03-03 14:00 |
Online | Online | Learning coefficients of normal mixture models in one dimension. Genki Watanabe, Ryuji Ito, Miki Aoyagi (Nihon Univ.) IBISML2020-48 |
Hierarchical learning models are widely used for data analysis in image or speech recognition, economics and so on. How... [more] | IBISML2020-48 pp.40-46 |
| IBISML | 2021-03-03 14:25 |
Online | Online | Markov Decision Processes for Simultaneous Control of Multiple Objects with Different State Transition Probabilities in Each Cluster Yuto Motomura, Akira Kamatsuka, Koki Kazama, Toshiyasu Matsushima (Waseda Univ.) IBISML2020-49 |
In this study, we propose an extended MDP model, which is a Markov decision process model with multiple control objects ... [more] | IBISML2020-49 pp.47-54 |
| IBISML | 2021-03-03 14:50 |
Online | Online | Safe reinforcement learning in high-dimensional continuous spaces Takumi Umemoto (NIT), Tohgoroh Matsui (Chubu Univ.), Atsuko Mutoh, Koich Moriyama, Inuzuka Nobuhiro (NIT) IBISML2020-50 |
We propose a method to extend the reinforcement learning method (CSEQ) based on success probability and profit in contin... [more] | IBISML2020-50 pp.55-62 |
| IBISML | 2021-03-03 15:15 |
Online | Online | Selective Inference for Change-point Detection in Multi-dimensional Series Data Ryota Sugiyama, Hiroki Toda, Vo Nguyen Le Duy, Yu Inatsu (NIT), Ichiro Takeuchi (NIT/RIKEN) IBISML2020-51 |
Detecting changes of the average structures in a multi-dimensional sequence is an important task in various fields. In t... [more] | IBISML2020-51 pp.63-70 |
| IBISML | 2021-03-04 09:05 |
Online | Online | IBISML2020-52 |
Experimental design (also known as the Design of experiments) is a systematic methodology for designing experiments to c... [more] | IBISML2020-52 p.71 |
| IBISML | 2021-03-04 09:45 |
Online | Online | IBISML2020-53 |
To determine a stopping timing of active learning is as important as determining an acquisition function. In this talk, ... [more] | IBISML2020-53 p.72 |
[Return to Top Page]