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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| CCS | 2025-03-18 10:20 |
Hokkaido | RUSUTSU RESORT (Hokkaido) | Implementation of stochastic outputs in BNN based on majority logic and its approximation error Kensei Suganuma, Hidehiro Nakano, Tomoyuki Sasaki (TCU) |
[more] | |
| CCS | 2025-03-18 13:45 |
Hokkaido | RUSUTSU RESORT (Hokkaido) | Multi-Agent Soft Actor Critic Reinforcement Learning with Different Temperature Parameters Shouki Otsuka, Hidehiro Nakano, Tomoyuki Sasaki (Tokyo City Univ) |
[more] | |
| CCS | 2025-03-18 17:00 |
Hokkaido | RUSUTSU RESORT (Hokkaido) | Dynamic Layer-Wise Neural Architecture Search with Growing and Pruning Kentaro Toki, Hidehiro Nakano, Tomoyuki Sasaki (Tokyo City Univ.) |
[more] | |
| CCS | 2025-03-19 09:00 |
Hokkaido | RUSUTSU RESORT (Hokkaido) | An Optimization Method for High-Dimensional Functions Based on Dimensional Decomposition by Graph Neural Networks Keito Sei, Hidehiro Nakano, Tomoyuki Sasaki (TCU) |
[more] | |
| NLP, CCS | 2024-06-06 15:20 |
Fukuoka | West Japan General Exhibition Center AIM (Fukuoka) | An Efficient Deep Learning Method by Sequential Insertion of Hidden Layers Kentaro Toki, Hidehiro Nakano (Tokyo City Univ.)  NLP2024-24 CCS2024-11 |
In recent deep learning, deep neural networks are trained using residual connections. However, training deep models requ... [more] | NLP2024-24 CCS2024-11 pp.44-48 |
| NLP, CCS | 2024-06-07 11:20 |
Fukuoka | West Japan General Exhibition Center AIM (Fukuoka) | Development of a processing hardware for a swarm intelligence algorithm based on spiking neural-oscillator networks Tomoyuki Sasaki (SIT), Hidehiro Nakano (TCU) NLP2024-33 CCS2024-20 |
Optimizer based on Spikin Neural-oscillator Networks (OSNNs) is one of the deterministic swarm intelligence algorithms. ... [more] | NLP2024-33 CCS2024-20 pp.84-89 |
| NLP, CCS | 2024-06-07 15:30 |
Fukuoka | West Japan General Exhibition Center AIM (Fukuoka) | ACO consisting of ants with various characteristics for solving time-dependent TSP Haruki Shikata, Hidehiro Nakano (Tokyo City Univ.) NLP2024-39 CCS2024-26 |
[more] | NLP2024-39 CCS2024-26 pp.113-116 |
| NLP, MSS | 2024-03-13 17:20 |
Misc. | Kikai-Shinko-Kaikan Bldg. (Misc.) | Application of Data Augmentation in Japanese Foundation Models Kazuki Era, Hidehiro Nakano (Tokyo City Univ.) MSS2023-84 NLP2023-136 |
One of the recent topics is data augmentation. Data augmentation is a method of augmenting training data to improve the ... [more] | MSS2023-84 NLP2023-136 pp.66-69 |
| NLP, MSS | 2024-03-14 10:25 |
Misc. | Kikai-Shinko-Kaikan Bldg. (Misc.) | A Particle Swarm Optimizer Based on Chaotic Spiking Oscillators with Dynamic Thresholds for Velocity Vectors Ahmed Ali, Hidehiro Nakano (Tokyo City Univ.) MSS2023-87 NLP2023-139 |
Particle Swarm Optimization (PSO) is a metaheuristic algorithm known for solving complex optimization problems. Despite ... [more] | MSS2023-87 NLP2023-139 pp.77-82 |
| CCS | 2023-11-12 10:25 |
Toyama | Toyama Prefectural University (Toyama) | Analysis of a simple network topology for optimizer based on spiking-neural oscillator networks Tomoyuki Sasaki (SIT), Hidehiro Nakano (TCU) CCS2023-34 |
Optimizer based on Spiking Neural-oscillator Networks (OSNNs) are one of the deterministic PSO methods, which are based ... [more] | CCS2023-34 pp.53-57 |
| CCS, NLP | 2023-06-08 15:35 |
Tokyo | Tokyo City Univ. (Tokyo) | 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) (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 (Hokkaido) | 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 | (Nagasaki, Online) (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 | (Nagasaki, Online) (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 | (Nagasaki, Online) (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 | (Mie, Online) (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 | (Mie, Online) (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 | (Mie, Online) (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 | (Mie, Online) (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 |
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