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Technical Committee on Information-Based Induction Sciences and Machine Learning (IBISML) (Searched in: 2021)
Search Results: Keywords 'from:2022-01-17 to:2022-01-17'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2022-01-17 10:00 |
Online | Online | Information Geometrically Generalized Covariate Shift Adaptation Masanari Kimura (SOKENDAI), Hideitsu Hino (ISM/RIKEN)  IBISML2021-18 |
[more] | IBISML2021-18 pp.1-8 |
| IBISML | 2022-01-17 10:20 |
Online | Online | Constrained Bayesian Optimization through Optimal-value Entropy Shion Takeno, Tomoyuki Tamura (NIT), Kazuki Shitara (Osaka Univ./JFCC), Masayuki Karasuyama (NIT) IBISML2021-19 |
The constrained optimization problem for the expensive black-box function is a major problem. Although the effectiveness... [more] | IBISML2021-19 pp.9-16 |
| IBISML | 2022-01-17 10:40 |
Online | Online | Automatic Makeup Transfer with GANs and Its Quantitative Evaluation Cuilin Wang, Jun'ichi Takeuchi (Kyushu Univ.) IBISML2021-20 |
Transferring makeup from a reference image with makeup to a source image without makeup has a wide range of application ... [more] | IBISML2021-20 pp.17-22 |
| IBISML | 2022-01-17 11:00 |
Online | Online | Cluster approximation in quantum Boltzmann machine based on information geometry Masaya Hoshikawa, Tomohiro Ogawa (UEC) IBISML2021-21 |
A Boltzmann Machine (BM) is a model of machine learning which consists of mutually connected probabilistic binary units... [more] |
IBISML2021-21 pp.23-28 |
| IBISML | 2022-01-17 11:20 |
Online | Online | CAMRI Loss: Class-wise Additive Angular Margin Loss for Improving Recall of a Specific Class Daiki Nishiyama (Univ. Tsukuba), Fukuchi Kazuto, Yohei Akimoto, Jun Sakuma (Univ. Tsukuba/RIKEN) IBISML2021-22 |
In real-world applications of multiclass classification models, there is a need to increase the recall of classes where ... [more] | IBISML2021-22 pp.29-36 |
| IBISML | 2022-01-17 13:05 |
Online | Online | [Invited Talk]
TBA Yuichi Kawamoto (Tohoku Univ.), Takahiro Ohyama (PSNRD) |
[more] | |
| IBISML | 2022-01-17 13:45 |
Online | Online | [Invited Talk]
Factor Analysis of Communication Quality through Machine Learning and Wireless LAN Sensing Koji Yamamoto (Kyoto Univ.) |
[more] | |
| IBISML | 2022-01-17 14:25 |
Online | Online | [Invited Talk]
Online Learning Based Solutions for B5G/6G Communication Systems Sherief Hashima (RIKEN) |
[more] | |
| IBISML | 2022-01-18 09:05 |
Online | Online | [Tutorial Lecture]
Introduction to Selective Inference Ichiro Takeuchi (Nitech/RIKEN) |
[more] | |
| IBISML | 2022-01-18 09:45 |
Online | Online | [Invited Talk]
TBA Shigeyuki Matsui (Nagoya Univ.) |
[more] | |
| IBISML | 2022-01-18 10:35 |
Online | Online | [Invited Talk]
TBA Atsushi Kawaguchi (Saga Univ.) |
[more] | |
| IBISML | 2022-01-18 11:15 |
Online | Online | [Invited Talk]
TBA Jun Sakuma (Tsukuba Univ./RIKEN) |
Explainability is one of the key elements required in medical image diagnosis using deep image recognition models. In th... [more] | |
| IBISML | 2022-01-18 13:00 |
Online | Online | Local Explanation of Graph Neural Network through Predictive Graph Mining Hinata Asahi, Masayuki Karasuyama (NIT) IBISML2021-23 |
Graph Neural Networks (GNNs) have attracted wide attention in the data science community. However, predictions of GNNs a... [more] | IBISML2021-23 pp.37-44 |
| IBISML | 2022-01-18 13:20 |
Online | Online | IBISML2021-24 |
We aim to explain a black-box classifier with the form: `data X is classified as class Y because X has A, B and does not... [more] | IBISML2021-24 pp.45-53 |
| IBISML | 2022-01-18 13:40 |
Online | Online | More Powerful Selective Inference for K-means clustering with Application to Single Cell Analysis Mizuki Sato, Yumehiro Omori, Yu Inatsu, Ichiro Takeuchi (NITech) IBISML2021-25 |
K-means clustering is the most famous clustering method because of its simplicity, and it has been applied to a wide ran... [more] | IBISML2021-25 pp.54-60 |
| IBISML | 2022-01-18 14:00 |
Online | Online | Robustness to Adversarial Examples by Mixtures of L1 Regularazation Models Hironobu Takenouchi, Junichi Takeuchi (Kyushu Univ.) IBISML2021-26 |
We propose a method of adversarial training using L1 regularizationfor image classification.It is known that L1 regulari... [more] | IBISML2021-26 pp.61-66 |
| IBISML | 2022-01-18 14:40 |
Online | Online | Domain Adaptation with Optimal Transport for Extended Variable Space Toshimitsu Atiake (ISM), Hideitsu Hino (ISM/RIKEN) IBISML2021-27 |
Domain adaptation aims to transfer knowledge of labeled instances obtained from a source domain to a target domain to fi... [more] | IBISML2021-27 pp.67-74 |
| IBISML | 2022-01-18 15:00 |
Online | Online | Bayesian Optimization for Simultaneous Optimization of Multiple Tasks with Max-value Entropy Search Rintaro Yamada, Shion Takeno, Masayuki Karasuyama (NIT) IBISML2021-28 |
Bayesian optimization (BO) has been widely studied as an effective approach to black-box optimizations. On the other han... [more] | IBISML2021-28 pp.75-80 |
| IBISML | 2022-01-18 15:20 |
Online | Online | Determining the number of clusters using the shrinking maximum likelihood self-organizing map Ryosuke Motegi, Yoichi Seki (Gunma Univ.) IBISML2021-29 |
Determining the number of clusters is one of the major challenges in clustering. The conventional method, such as the Ex... [more] | IBISML2021-29 pp.81-87 |
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