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Technical Committee on Information-Based Induction Sciences and Machine Learning (IBISML) (Searched in: 2022)
Search Results: Keywords 'from:2022-12-22 to:2022-12-22'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2022-12-22 10:10 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
 IBISML2022-40 |
Supervised learning, the core technology for current "AI", predicts the future as an extension of the experienced past a... [more] | IBISML2022-40 p.1 |
| IBISML | 2022-12-22 10:50 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
IBISML2022-41 |
Materials informatics, a fusion of materials science and information science, has been attracting attention in recent ye... [more] | IBISML2022-41 p.2 |
| IBISML | 2022-12-22 13:00 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
IBISML2022-42 |
[more] | IBISML2022-42 p.3 |
| IBISML | 2022-12-22 13:40 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
IBISML2022-43 |
In recent years, materials science fields have been conducting efficient materials development through informatics-in th... [more] | IBISML2022-43 pp.4-5 |
| IBISML | 2022-12-22 14:30 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Serendipity-aware niche content recommendation system Shogo Kawasaki, (KIT) IBISML2022-44 |
In recent years, the Internet has seen the proliferation of e-commerce sites such as Amazon and Rakuten, and numerous di... [more] | IBISML2022-44 pp.6-13 |
| IBISML | 2022-12-22 14:50 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Prediction of Answers Considering Characteristics Yo Ehara (TGU) IBISML2022-45 |
[more] | IBISML2022-45 pp.14-17 |
| IBISML | 2022-12-22 15:10 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Effect of Prior Distribution when Data-Generating Process is in a Neighborhood of Singularities of Learning Machines Nozomi Maki, Sumio Watanabe (TokyoTech) IBISML2022-46 |
Learning machines which have hierarchical structure or latent variables such as deep learning or normal mixtures contain... [more] | IBISML2022-46 pp.18-23 |
| IBISML | 2022-12-22 15:30 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
[Short Paper]
Semi supervised image classification using unreliable pseudo label Jihong Hu, Yinhao Li, Yen-Wei Chen (Ritsumeikan Univ.) IBISML2022-47 |
Semi-supervised learning (SSL), which automatically annotates unlabeled data with pseudo labels during training, has ach... [more] | IBISML2022-47 pp.24-29 |
| IBISML | 2022-12-22 15:55 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
IBISML2022-48 |
[more] | IBISML2022-48 pp.30-37 |
| IBISML | 2022-12-22 16:15 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
IBISML2022-49 |
Gaussian process upper confidence bound (GP-UCB) is a theoretically promising black-box optimization method; however, a ... [more] | IBISML2022-49 pp.38-45 |
| IBISML | 2022-12-22 16:35 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Implementation and evaluation of Noisy Nets to Reinforcement learning of Automated Designing ICT System Tianchen Zhou (Sophia Univ.), Yutaka Yakuwa (NEC), Natsuki Okamura (Sophia Univ.), Takayuki Kuroda (NEC), Ikuko E. Yairi (Sophia Univ.) IBISML2022-50 |
This paper introduces a reinforcement learning method for the ICT system design process. Since the state space of the de... [more] | IBISML2022-50 pp.46-53 |
| IBISML | 2022-12-23 09:20 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Embedding stochastic differential equations into neural networks using duality Naoki Sugishita, Jun Ohkubo (Saitama Univ.) IBISML2022-51 |
Neural network training requires a large amount of data. However, sometimes we have information on the underlying equati... [more] | IBISML2022-51 pp.54-61 |
| IBISML | 2022-12-23 09:40 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Effect of memory unit initialization on performance for function approximation Yuto Terasawa, Jun Ohkubo (Saitama Univ.) IBISML2022-52 |
Many researchers have proposed various neural network models for learning time-series data, such as RNN, LSTM, and Trans... [more] | IBISML2022-52 pp.62-69 |
| IBISML | 2022-12-23 10:00 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Zero-shot domain adaptation based on dual-level mix and contrast Yu Zhe, Fukuchi Kazuto, Sakuma Jun (Tsukuba Univ/Riken AIP) IBISML2022-53 |
[more] | IBISML2022-53 pp.70-77 |
| IBISML | 2022-12-23 10:30 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Statistically Significant Concept-based Explanation via Model Knockoffs Kaiwen Xu, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma (Univ. Tsukuba/RIKEN AIP) IBISML2022-54 |
[more] | IBISML2022-54 pp.78-85 |
| IBISML | 2022-12-23 10:50 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Interpretable Deep Image Classifier with Class-distinguishable Concept Text Kazuhiro Saito, Kazuto Fukuchi (Univ.Tsukuba), Jun Sakuma (Univ.Tsukuba/RIKEN) IBISML2022-55 |
(To be available after the conference date) [more] | IBISML2022-55 pp.86-93 |
| IBISML | 2022-12-23 11:10 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Enhancement of Audio Signals Using Learning from Positive and Unlabelled Data Nobutaka Ito, Masashi Sugiyama (UTokyo) IBISML2022-56 |
Audio signal enhancement (SE) is the task of extracting a desired class of sounds (a “signal”) from an observed sound mi... [more] | IBISML2022-56 pp.94-100 |
| IBISML | 2022-12-23 13:00 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Initial evaluation of node embedding using quantum walk Rei Sato, Shuichiro Haruta, Kazuhiro Saito, Mori Kurokawa (KDDI Research, Inc.) IBISML2022-57 |
DeepWalk is one of the node-embedding methods which represents node features using sequences obtained from a random walk... [more] | IBISML2022-57 pp.101-105 |
| IBISML | 2022-12-23 13:20 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Improvement of Defect Detection Accuracy by Background Subtraction Method Using CycleGAN Sota Sugiyama, Naoyuki Aikawa (TUS) IBISML2022-58 |
[more] | IBISML2022-58 pp.106-111 |
| IBISML | 2022-12-23 13:40 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Multi-objective Bayesian Optimization for Identifying Distributionally-robust Pareto-frontier Yu Inatsu (Nitech), Ichiro Takeuchi (Nagoya Univ./RIKEN) IBISML2022-59 |
Pareto optimization is one of the multi-objective optimization problems for multiple black-box functions. Recently, an o... [more] | IBISML2022-59 pp.112-119 |
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