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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 99  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
NLP, MSS 2024-03-13
17:20
Misc. Kikai-Shinko-Kaikan Bldg. 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. 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 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. 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
 Results 1 - 20 of 99  /  [Next]  
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