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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| CCS | 2023-03-27 09:00 |
Hokkaido | RUSUTSU RESORT | Medical Image Segmentation with Inverse Heat Dissipation Model Yu Kashihara, Takashi Matsubara (Osaka Univ.)  CCS2022-82 |
The diffusion model is a generative model based on stochastic transitions and has been successfully used to generate an... [more] |
CCS2022-82 pp.107-112 |
| CCS | 2023-03-27 09:20 |
Hokkaido | RUSUTSU RESORT | Learning Commutative Vector Field in Latent Space of Deep Generative Model Takehiro Aoshima, Takashi Matsubara (Osaka Univ.) CCS2022-83 |
Deep generative models, such as generative adversarial networks (GANs), are known for generating high-quality images. Ho... [more] | CCS2022-83 pp.113-116 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2022-06-27 15:55 |
Okinawa | (Primary: On-site, Secondary: Online) |
NC2022-5 IBISML2022-5 |
(To be available after the conference date) [more] | NC2022-5 IBISML2022-5 pp.47-52 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2022-06-28 10:05 |
Okinawa | (Primary: On-site, Secondary: Online) |
Learning Attribute Vector Fields in GAN Latent Space Takehiro Aoshima, Takashi Matsubara (Osaka Univ.) NC2022-12 IBISML2022-12 |
Generative Adversarial Networks (GANs) can generate a great variety of high-quality images. Despite their ability to g... [more] |
NC2022-12 IBISML2022-12 pp.94-99 |
| CCS | 2022-03-27 15:15 |
Hokkaido | RUSUTSU RESORT HOTEL & CONVENTION (Primary: On-site, Secondary: Online) |
Learning Physical Systems with Imbalance-Aware Deep Learning Takahito Yoshida (Osaka Univ.), Takaharu Yaguchi (Kobe Univ.), Takashi Matsubara (Osaka Univ.) CCS2021-47 |
[more] | CCS2021-47 pp.66-71 |
| CCS | 2022-03-27 15:40 |
Hokkaido | RUSUTSU RESORT HOTEL & CONVENTION (Primary: On-site, Secondary: Online) |
Evaluation of Industrial Anomaly Detection using Diffusion Model Yu Kashihara, Takashi Matsubara (Osaka Univ.) CCS2021-48 |
Anomaly detection by generative models is achieved by comparing the reconstruction and the original image. However, exis... [more] | CCS2021-48 pp.72-77 |
| CCS | 2022-03-27 16:05 |
Hokkaido | RUSUTSU RESORT HOTEL & CONVENTION (Primary: On-site, Secondary: Online) |
Range-Equivariant Convolution for Spherical Projection-based Segmentation of LiDAR Point Clouds Hidetaka Marumo, Takashi Matsubara (Osaka Univ) CCS2021-49 |
In autonomous driving, LiDAR point clouds segmentation has attracted much attention. For efficiency and ease of design, ... [more] | CCS2021-49 pp.78-83 |
| 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 |
| RECONF, VLD, CPSY, IPSJ-ARC, IPSJ-SLDM [detail] | 2022-01-25 09:30 |
Online | Online | GPU acceleration of algorithm for minimal distance approximate calculation between two objects Masumi Fukuta, Takakazu Kurokawa, Takashi Matsubara, Keisuke Iwai (NDA) VLD2021-62 CPSY2021-31 RECONF2021-70 |
(To be available after the conference date) [more] | VLD2021-62 CPSY2021-31 RECONF2021-70 pp.73-77 |
| CCS | 2021-11-18 16:25 |
Osaka | Osaka Univ. (Primary: On-site, Secondary: Online) |
Memory Efficient Training of Neural ODE by Symplectic Adjoint Method Takashi Matsubara, Yuto Miyatake (Osaka Univ.), Takaharu Yaguchi (Kobe Univ.) CCS2021-23 |
Neural ODE learns an ordinary differential equation using neural networks, thereby modeling a continuous-time dynamics a... [more] | CCS2021-23 pp.31-36 |
| CCS | 2021-11-19 11:10 |
Osaka | Osaka Univ. (Primary: On-site, Secondary: Online) |
Toward Human Cognition-inspired High-Level Decision Making For Hierarchical Reinforcement Learning Agents Rousslan Fernand Julien Dossa (Kobe Univ.), Takashi Matsubara (Osaka Univ.) CCS2021-28 |
Hierarchical reinforcement learning (HRL) methods aim to leverage the concept of temporal abstraction to efficiently sol... [more] | CCS2021-28 pp.61-66 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2021-06-28 13:25 |
Online | Online | Training Neural ODE by Symplectic Integrator Takashi Matsubara, Yuto Miyatake (Osaka Univ.), Takaharu Yaguchi (Kobe Univ.) NC2021-2 IBISML2021-2 |
A differential equation model using neural networks, neural ODE, enables use to model a continuous-time dynamics and pr... [more] | NC2021-2 IBISML2021-2 pp.9-14 |
| MI | 2021-03-15 15:15 |
Online | Online | Deep State-Space Modeling of FMRI Images with Disentangle Attributes Koki Kusano (Kobe Univ.), Takashi Matsubara (Osaka Univ.), Kuniaki Uehara (Osaka Gakuin Univ.) MI2020-59 |
As well as the disorder and other targets, nuisance attributes such as age, gender, and scanner specifications underlie ... [more] | MI2020-59 pp.56-61 |
| MI | 2021-03-17 13:45 |
Online | Online | Medical Image Style Translation by Adversarial Training with Paired Inputs Kazuki Fujioka (Kobe Univ.), Takashi Matsubara (Osaka Univ.), Kuniaki Uehara (Osaka Gakuin Univ.) MI2020-96 |
Medical image diagnosis by artificial intelligence requires a large amount of data for learning. However, preparing such... [more] | MI2020-96 pp.212-217 |
| EA, ASJ-H, EMM | 2020-11-20 09:00 |
Online | Online | [Poster Presentation]
Improving wavelet-synchrosqueezing transform with calculating angular frequency using time shift Akira Kakutani, Takashi Matsubara, Keisuke Iwai, Takakazu Kurokawa (NDA) EA2020-21 EMM2020-36 |
Time-frequency analysis is mainly used for analyzing acoustic signals. Short-time Fourier transform by Allen, et al. and... [more] | EA2020-21 EMM2020-36 pp.1-5 |
| IBISML | 2020-10-20 10:25 |
Online | Online | Few-shot Anomaly Detection by Extracting Common Feature of Set Data Kazuki Sato (Kobe Univ.), Satoshi Nakata (The KAITEKI Institute), Takashi Matsubara (Osaka Univ.), Kuniaki Uehara (Osaka Gakuin Univ.) IBISML2020-9 |
[more] | IBISML2020-9 pp.8-13 |
| IN, CCS (Joint) |
2020-08-03 15:35 |
Online | Online | [Invited Talk]
Current Status and Future Prospects for Image-Based Anomaly Detection Kazuki Sato (Kobe Univ.), Takashi Matsubara (Osaka Univ.), Kuniaki Uehara (Osaka Gakuin Univ.) CCS2020-12 |
[more] | CCS2020-12 pp.1-4 |
| IBISML | 2020-03-11 09:45 |
Kyoto | Kyoto University (Cancelled but technical report was issued) |
Knowledge Graph Completion by Separating Transition and Score Functions Kenta Hama, Takashi Matsubara, Kuniaki Uehara (Kobe Univ.) IBISML2019-41 |
A knowledge graph is represented by a set of two entities and the relations, and used for various tasks such as informat... [more] | IBISML2019-41 pp.59-62 |
| IBISML | 2020-03-11 15:10 |
Kyoto | Kyoto University (Cancelled but technical report was issued) |
Fairness Causes Vulnerability to Adversarial Attacks Koki Wataoka, Takashi Matsubara, Kuniaki Uehara (Kobe Univ.) IBISML2019-48 |
When using machine learning models in society, it is essential to be ensure classifiers are fair to race and gender. In ... [more] | IBISML2019-48 pp.101-105 |
| IPSJ-SLDM, RECONF, VLD, CPSY, IPSJ-ARC [detail] | 2020-01-24 11:25 |
Kanagawa | Raiosha, Hiyoshi Campus, Keio University | Implementation of high speed rainbow table generation using Keccak hashing algorithm on CUDA Nguyen Dat Thuong, Keisuke Iwai, Takashi Matsubara, Takakazu Kurokawa (NDA) VLD2019-84 CPSY2019-82 RECONF2019-74 |
This paper proposes the implementation of high speed rainbow table generation using Keccak hashing algorithm with the in... [more] | VLD2019-84 CPSY2019-82 RECONF2019-74 pp.181-186 |
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