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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| CCS | 2023-11-12 10:25 |
Toyama | Toyama Prefectural University | Investigation of effectiveness for Ring 1-way network in swarm intelligence algorithm based on spiking neural-oscillator networks Tomoyuki Sasaki (SIT), Hidehiro Nakano (TCU) |
[more] | |
| CCS, NLP | 2023-06-08 15:35 |
Tokyo | Tokyo City Univ. | A place and route method in AQFP circuits using multi-objective optimization Syota Kasai, Hidehiro Nakano (Tokyo City Univ.)  NLP2023-18 CCS2023-6 |
In recent years, research has been conducted on AQFP circuits, which are superconducting logic circuits that consume les... [more] | NLP2023-18 CCS2023-6 pp.21-24 |
| NLP | 2023-05-13 11:15 |
Fukushima | Kenshin Koriyama Cultural Center (Koriyama, Fukushima) | Parameter adjustment methods of ACO based on moving costs in time-dependent TSP Teppei Yamauchi, Hidehiro Nakano (Tokyo City Univ.) NLP2023-4 |
The Time Dependent Traveling Salesman Problem (TDTSP) is a combinatorial optimization problem with dynamically changing ... [more] | NLP2023-4 pp.16-19 |
| CCS | 2023-03-26 13:35 |
Hokkaido | RUSUTSU RESORT | Analysis of learning performance in CycleGAN by applying data augmentation to few data Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-72 |
In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] | CCS2022-72 pp.54-58 |
| NLP, MSS | 2023-03-17 15:45 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Improving Recognition Accuracy in Contrastive Learning by Weighted Similarity Based on Data Source Ryotaro Sei, Hidehiro Nakano (Tokyo City Univ.) MSS2022-107 NLP2022-152 |
[more] | MSS2022-107 NLP2022-152 pp.214-219 |
| NLP, MSS | 2023-03-17 16:05 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Lightweighting Noisy Student Semi-Supervised Learning by Applying MobileNet Yuga Morishima, Hidehiro Nakano (Tokyo City Univ.) MSS2022-108 NLP2022-153 |
Recently, Convolutional Neural Networks (CNNs) have attracted much attention in various fields such as image classificat... [more] | MSS2022-108 NLP2022-153 pp.220-224 |
| NLP, MSS | 2023-03-17 16:25 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Investigation on improving diversity of options in option-critic reinforcement learning Aya Nakagawa, Hidehiro Nakano (Tokyo City Univ.) MSS2022-109 NLP2022-154 |
Recently, reinforcement learning has been attracting attention in various fields such as automatic control and game AI. ... [more] | MSS2022-109 NLP2022-154 pp.225-230 |
| CCS | 2022-11-18 14:30 |
Mie | (Primary: On-site, Secondary: Online) |
Particle swarm optimization considering a positive and negative inertia terms by Levy distribution Sohei Kusaka, Hidehiro Nakano (Tokyo City Univ.) CCS2022-57 |
Particle Swarm Optimization (PSO) is known as a type of swarm intelligence algorithms. The inertia constant of each sear... [more] | CCS2022-57 pp.71-75 |
| CCS | 2022-11-18 14:55 |
Mie | (Primary: On-site, Secondary: Online) |
Particle swarm optimization using bit representation of state variables as random dynamics Masashi Nitanda, Hidehiro Nakano (Tokyo City Univ.) CCS2022-58 |
[more] | CCS2022-58 pp.76-80 |
| CCS | 2022-11-18 15:20 |
Mie | (Primary: On-site, Secondary: Online) |
Multi-domain translation from few data by CycleGAN applying data augmentation Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-59 |
In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] | CCS2022-59 pp.81-84 |
| CCS | 2022-11-18 16:00 |
Mie | (Primary: On-site, Secondary: Online) |
Investigation for the coupling interactions in swarm intelligence algorithm based on spiking neural-oscillator networks Tomoyuki Sasaki (SIT), Hidehiro Nakano (TCU) CCS2022-60 |
Optimizer based on Spiking Neural-oscillator Networks (OSNNs) are deterministic swarm intelligence algorithms which intr... [more] | CCS2022-60 pp.85-90 |
| IN, CCS (Joint) |
2022-08-04 10:00 |
Hokkaido | Hokkaido University(Centennial Hall) (Primary: On-site, Secondary: Online) |
Investigation on Applying Data Augmentation to CycleGAN Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-26 |
In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] | CCS2022-26 pp.1-5 |
| CCS, NLP | 2022-06-09 13:50 |
Osaka | (Primary: On-site, Secondary: Online) |
Improvement of Learning Performance by Using a Symmetric Constraint Condition in PPO Naoki Iwaya, Hidehiro Nakano (Tokyo City Univ.) NLP2022-3 CCS2022-3 |
Deep Reinforcement Learning (DRL) is an algorithm of learning the optimal action from the experiences. PPO KL Penalty, a... [more] | NLP2022-3 CCS2022-3 pp.13-16 |
| CCS, NLP | 2022-06-09 14:15 |
Osaka | (Primary: On-site, Secondary: Online) |
Improvement of Recognition Accuracy by Sequential Execution of Unsupervised Learning and Semi-supervised Learning Hiroki Murakami, Hidehiro Nakano (Tokyo City Univ.) NLP2022-4 CCS2022-4 |
In this study, we propose a sequential learning method that improves recognition accuracy by alternately utilizing the k... [more] | NLP2022-4 CCS2022-4 pp.17-22 |
| CCS, NLP | 2022-06-09 14:55 |
Osaka | (Primary: On-site, Secondary: Online) |
Basic Performance of CNNs Using Dynamic Filters Based on Octave Convolution Kiyotaka Matono, Hidehiro Nakano (Tokyo City Univ.) NLP2022-5 CCS2022-5 |
The methods of using dynamic filters for convolutional neural networks (CNNs) have attracted attentions. In recent years... [more] | NLP2022-5 CCS2022-5 pp.23-26 |
| CCS, NLP | 2022-06-09 15:20 |
Osaka | (Primary: On-site, Secondary: Online) |
Speeding-up by Reduction of Processing Paths in Octave Convolution Akito Yoshikawa, Hidehiro Nakano (Tokyo City Univ.) NLP2022-6 CCS2022-6 |
Octave Convolution (OctConv), one of the convolutional neural network methods, can also improve accuracy while reducing ... [more] | NLP2022-6 CCS2022-6 pp.27-30 |
| CCS, NLP | 2022-06-10 15:55 |
Osaka | (Primary: On-site, Secondary: Online) |
Swarm intelligence algorithm based on spiking neural-oscillator networks, coupling interactions and solving performances Tomoyuki Sasaki (SIT), Hidehiro Nakano (TCU) NLP2022-22 CCS2022-22 |
Optimizer based on spiking neural-oscillator networks (OSNN) are one of the deterministic swarm intelligence algorithms... [more] |
NLP2022-22 CCS2022-22 pp.111-116 |
| NLP | 2021-12-17 10:00 |
Oita | J:COM Horuto Hall OITA | Basic performances of a swarm intelligence algorithm based on spiking oscillator networks Tomoyuki Sasaki (SIT), Hidehiro Nakano (TCU) NLP2021-43 |
Spiking oscillator networks are simply coupling systems of plural spiking oscillators, which generate various synchroniz... [more] | NLP2021-43 pp.1-6 |
| NLP | 2021-12-17 10:50 |
Oita | J:COM Horuto Hall OITA | Reduction of Computation Cost for Self-Attention Networks Using Octave Convolution Jun Kokubo, Hidehiro Nakano (Tokyo City Univ.) NLP2021-45 |
[more] | NLP2021-45 pp.13-17 |
| NLP | 2021-12-17 11:15 |
Oita | J:COM Horuto Hall OITA | Investigation on Distance Between Probability Distributions in Trust Region Policy Optimization Kenta Sugaya, Hidehiro Nakano (Tokyo City Univ.) NLP2021-46 |
In this paper, we propose a method to change Kullback-Leibler Divergence to Jensen-Shannon Divergence that used in Trust... [more] | NLP2021-46 pp.18-21 |
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