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原著論文・国際会議論文著書・解説論文講演発表学位論文ニュース記事研究助成・共同研究

原著論文・国際会議論文

2025

原著論文

  1. Takashi Matsubara, Takehiro Aoshima, Ai Ishikawa, and Takaharu Yaguchi, "Deep Energy-Based Discrete-Time Physical Model for Reproducing Energetic Behavior," IEEE Transactions on Neural Networks and Learning Systems, 2025. (accepted) paper

国際会議論文

  1. Razmik Arman Khosrovian, Takaharu Yaguchi, Hiroaki Yoshimura, and Takashi Matsubara, "Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains," The Thirteenth International Conference on Learning Representations (ICLR2025), Singapore, Apr. 2025. (accepted) arXiv paper
  2. Takashi Matsubara and Takaharu Yaguchi, "Number Theoretic Accelerated Learning of Physics-Informed Neural Networks," The Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI2025), Philadelphia, 28 Feb. 2025. (oral) arXiv

ワークショップ

  1. Shinnosuke Saito and Takashi Matsubara, "Image Interpolation with Score-based Riemannian Metrics of Diffusion Models," ICLR 2025 Workshop on Deep Generative Model in Machine Learning: Theory, Principle and Efficacy (DeLTa), Singapore, 28 Apr. 2025. link
  2. Razmik Khosrovian, Takaharu Yaguchi, Hiroaki Yoshimura, and Takashi Matsubara, "Modeling Coupled Systems by Neural Networks with Poisson Structures and Ports," International Conference on Scientific Computing and Machine Learning 2025 (SCML2025), Kyoto, 7 Mar. 2025. (oral) link
  3. Baige Xu, Yusuke Tanaka, Takashi Matsubara, and Takaharu Yaguchi, "Learning Hamiltonian Density Using DeepONet for Modeling Wave Equations," International Conference on Scientific Computing and Machine Learning 2025 (SCML2025), Kyoto, 6 Mar. 2025. link
  4. Yeang Makara, Yusuke Tanaka, Takashi Matsubara, and Takaharu Yaguchi, "Learning Hamiltonian Partial Differential Equations Using DeepONet With A Symplectic Branch Network," International Conference on Scientific Computing and Machine Learning 2025 (SCML2025), Kyoto, 6 Mar. 2025. link

2024

原著論文

  1. Takahito Yoshida, Takaharu Yaguchi, and Takashi Matsubara, "Loss Function for Deep Learning to Model Dynamical Systems," IEICE Transactions on Information and Systems, vol. E107-D, no. 11, pp. 1458-1462, 2024. paper
  2. Takashi Matsubara, Yuto Miyatake, and Takaharu Yaguchi, "The Symplectic Adjoint Method: Memory-Efficient Backpropagation of Neural-Network-Based Differential Equations," IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 8, pp. 10526-10538, 2024. paper
  3. Hidetaka Marumo and Takashi Matsubara, "Scale-Equivariant Convolution for Semantic Segmentation of Depth Image," Nonlinear Theory and Its Applications, IEICE, vol. 15, no. 1, pp. 36-53, 2024. paper

国際会議論文

  1. Kota Sueyoshi and Takashi Matsubara, "Predicated Diffusion: Predicate Logic-Based Attention Guidance for Text-to-Image Diffusion Models," The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (CVPR2024), Seattle, 19 Jun. 2024. (highlight) arXiv paper

ワークショップ

  1. Razmik Arman Khosrovian, Takaharu Yaguchi, and Takashi Matsubara, "Port-Hamiltonian Neural Networks for Learning Coupled Systems and Their Interactions," NeurIPS 2024 Workshop on Machine Learning and the Physical Sciences, Vancouver, 15 Dec. 2024. link
  2. Yosuke Nishimoto and Takashi Matsubara, "Transformer-based Imagination with Slot Attention," NeurIPS 2024 Workshop on Compositional Learning, Vancouver, 15 Dec. 2024. link
  3. Keigo Tsutsui, Phuoc Thanh Tran-Ngoc, Hirotaka Sato, and Takashi Matsubara, "Deep Dynamics Modeling of Interactions in Collective Behaviors of Insects," 2024 International Symposium on Nonlinear Theory and Its Applications (NOLTA2024), Ha Long, 3 Dec. 2024. (Student Paper Award)
  4. Razmik Arman Khosrovian, Takaharu Yaguchi, and Takashi Matsubara, "Learning Coupled Systems and their Connectivity Using Port-Hamiltonian Neural Networks," CAI2024 Workshop on Scientific Machine Learning and Its Industrial Applications (SMLIA2024), Singapore, 25 Jun. 2024. link
  5. Keigo Tsutsui, Phuoc Thanh Tran-Ngoc, Hirotaka Sato, and Takashi Matsubara, "Deep Dynamics Modeling of Interactions in Insect Group Behavior," CAI2024 Workshop on Scientific Machine Learning and Its Industrial Applications (SMLIA2024), Singapore, 25 Jun. 2024. link

2023

原著論文

  1. 優森川, 中西波瑠, 稲村直樹, 近藤伸明, 小渕浩希, 大澤輝夫, 松原崇, 申吉浩, 大島裕明, 上原邦昭, "船舶における全天球画像のデータ収集と雲形・状態・全雲量の自動判定の試み," 日本気象学会機関誌「天気」, vol. 70, no. 12, pp. 577-692, 2023. paper
  2. Yu Kashihara and Takashi Matsubara, "Inverse Heat Dissipation Model for Medical Image Segmentation," IEICE Transactions on Information and Systems, vol. E106-D, no. 11, pp. 1930-1934, 2023. paper
  3. Zheng Chen, Ziwei Yang, Lingwei Zhu, Peng Gao, Takashi Matsubara, Shigehiko Kanaya, and MD Altaf-Ul-Amin, "Learning Vector Quantized Representation for Cancer Subtypes Identification," Computer Methods and Programs in Biomedicine, 107543, 2023. paper
  4. Kenta Hama and Takashi Matsubara, "Multi-Modal Entity Alignment Using Uncertainty Quantification for Modality Importance," IEEE Access, 2023. paper

国際会議論文

  1. Takehiro Aoshima and Takashi Matsubara, "Deep Curvilinear Editing: Commutative and Nonlinear Image Manipulation for Pretrained Deep Generative Model," The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (CVPR2023), Vancouver, Jun. 2023. paper arXiv
  2. Takashi Matsubara and Takaharu Yaguchi, "FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities," The Eleventh International Conference on Learning Representations (ICLR2023), Kigali, May 2023. paper arXiv
  3. Zheng Chen, Lingwei Zhu, Ziwei Yang, and Takashi Matsubara, "A Two-View EEG Representation for Brain Cognition by Composite Temporal-Spatial Contrastive Learning," SIAM International Conference on Data Mining (SDM23), Minneapolis, Apr. 2023. paper

ワークショップ

  1. Keigo Tsutsui, Phuoc Thanh Tran-Ngoc, Hirotaka Sato, and Takashi Matsubara, "Deep-Learning-Based Time-Series Analysis of Insect Behavior," 2023 International Symposium on Nonlinear Theory and Its Applications (NOLTA2023), Catania, Sep. 2023.
  2. Hidetaka Marumo and Takashi Matsubara, "Scale-Equivariant Convolution for Projection-based Point Cloud Segmentation," 2023 International Symposium on Nonlinear Theory and Its Applications (NOLTA2023), Catania, Sep. 2023.
  3. Kota Sueyoshi and Takashi Matsubara, "Concept Composition by Energy-Based Model using Order Embedding," 2023 International Symposium on Nonlinear Theory and Its Applications (NOLTA2023), Catania, Sep. 2023.
  4. Baige Xu, Takashi Matsubara, and Takaharu Yaguchi, "Application of the neural operator for physical simulations of GENERIC systems," 2023 International Symposium on Nonlinear Theory and Its Applications (NOLTA2023), Catania, Sep. 2023.
  5. Yuhan Chen, Takashi Matsubara, and Takaharu Yaguchi, "Super-resolution of numerical solutions of nonlinear elliptic equations by DeepONet," 2023 International Symposium on Nonlinear Theory and Its Applications (NOLTA2023), Catania, Sep. 2023.
  6. Noa Ogawa, Yuhan Chen, Takashi Matsubara, and Takaharu Yaguchi, "Generalization Error Analysis of Discrete Hamiltonian Neural Networks," 2023 International Symposium on Nonlinear Theory and Its Applications (NOLTA2023), Catania, Sep. 2023.
  7. Takashi Matsubara and Takaharu Yaguchi, "Good Lattice Accelerates Physics-Informed Neural Networks," ICML2023 Workshop on the Synergy of Scientific and Machine Learning Modeling (SynS and ML), Honolulu, Jun. 2023. link
  8. Baige Xu, Yuhan Chen, Takashi Matsubara, and Takaharu Yaguchi, "Equivalence Class Learning for GENERIC Systems," ICML2023 Workshop on New Frontiers in Learning, Control, and Dynamical Systems (Frontiers4LCD), Honolulu, Jun. 2023. link
  9. Yuhan Chen, Baige Xu, Takashi Matsubara, and Takaharu Yaguchi, "Variational Principle and Variational Integrators for Neural Symplectic Forms," ICML2023 Workshop on New Frontiers in Learning, Control, and Dynamical Systems (Frontiers4LCD), Honolulu, Jun. 2023. link
  10. Takahito Yoshida, Takaharu Yaguchi, and Takashi Matsubara, "On Loss Function for Deep Learning of Physical Systems," RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2023), Honolulu, Feb. 2023.
  11. Yu Kashihara and Takashi Matsubara, "Inverse Heat Dissipation Model for Image Segmentation," RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2023), Honolulu, Feb. 2023.
  12. Takehiro Aoshima and Takashi Matsubara, "Learning Attribute Curvilinear Coordinates for Pretrained Deep Generative Model," RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2023), Honolulu, Feb. 2023.

2022

原著論文

  1. 西井裕亮, 宮崎淳吾, 篠崎教志, 高松哲哉, 松原崇, 平田豊, "次世代モビリティに向けた眼球運動からの集中度推定," 応用数理, インダストリアルマテリアルズ, vol. 32, no.3, pp. 31-35, 2022. paper
  2. Takashi Matsubara, Kazuki Sato, Kenta Hama, Ryosuke Tachibana, and Kuniaki Uehara, "Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity," IEEE Transactions on Cybernetics, vol. 52, no. 6, pp. 5161-5173, 2022. paper arXiv
  3. Takumi Kimura, Takashi Matsubara, and Kuniaki Uehara, "Topology-Aware Flow-Based Point Cloud Generation," IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 11, pp. 7967-7982, 2022. paper
  4. Kazuki Sato, Satoshi Nakata, Takashi Matsubara, and Kuniaki Uehara, "Few-shot Anomaly Detection using Deep Generative Models for Grouped Data," IEICE Transactions on Information and Systems, vol.E105-D, no.2, pp.436-440, 2022. paper

国際会議論文

  1. Zheng Chen, Lingwei Zhu, Ziwei Yang, and Takashi Matsubara, "Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization," the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD2022), Grenoble, Sep. 2022. paper
  2. Yuhan Chen, Takashi Matsubara, and Takaharu Yaguchi, "KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-Zero Training Loss," The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI2022), Virtual, Feb. 2022. (oral) paper arXiv

ワークショップ

  1. Takehiro Aoshima and Takashi Matsubara, "Nonlinear and Commutative Editing in Pretrained GAN Latent Space," NeurIPS 2022 Workshop on NeurReps, New Orleans, Nov. 2022. link
  2. Yuhan Chen, Takashi Matsubara, and Takaharu Yaguchi, "Variational Integrator for Hamiltonian Neural Networks," 2022 International Symposium on Nonlinear Theory and Its Applications (NOLTA2022), Online, Dec. 2022, A2L-D-02. (Student Paper Award)
  3. Baige Xu, Yuhan Chen, Takashi Matsubara, and Takaharu Yaguchi, "Learning Generic Systems Using Neural Symplectic Forms," 2022 International Symposium on Nonlinear Theory and Its Applications (NOLTA2022), Online, Dec. 2022, A2L-D-03..
  4. Yu Kashihara, and Takashi Matsubara, "Application of Denoising Image Restoration to Anomaly Detection," 2022 International Symposium on Nonlinear Theory and Its Applications (NOLTA2022), Online, Dec. 2022, B2L-B-01..
  5. Kenta Hama, and Takashi Matsubara, "Common Space Learning with Gaussian Embedding for Multi-Modal Entity Alignment," 2022 International Symposium on Nonlinear Theory and Its Applications (NOLTA2022), Online, Dec. 2022, B3L-E-02. (Student Paper Award)
  6. Rousslan Fernand Julien Dossa, Takashi Matsubara, "Toward Human Cognition-inspired High-Level Decision Making For Hierarchical Reinforcement Learning Agents," ICML2022 Workshop on the Decision Awareness in Reinforcement Learning, Virtual/Maryland, Jul. 2022. link
  7. Takahito Yoshida, Takaharu Yaguchi, and Takashi Matsubara, "Imbalance-Aware Learning for Deep Physics Modeling," ICLR2022 Workshop on AI for Earth and Space Science (ai4earth), Virtual, Apr. 2022. link

2021

原著論文

  1. Rousslan Fernand Julien Dossa, Shengyi Huang, Santiago Ontañón, Takashi Matsubara, "An Empirical Investigation of Early Stopping Optimizations in Proximal Policy Optimization," IEEE Access, vol. 9, pp. 117981-117992, 2021. paper
  2. Kenta Hama, Takashi Matsubara, Kuniaki Uehara, Jianfei Cai, "Exploring Uncertainty Measures for Image-Caption Embedding-and-Retrieval Task," ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 17, no. 2, article no. 46, 2021. paper arXiv
  3. Takashi Matsubara, Koki Kusano, Tetsuo Tashiro, Ken'ya Ukai, Kuniaki Uehara, "Deep Generative Model of Individual Variability in fMRI Images of Psychiatric Patients," IEEE Transactions on Biomedical Engineering, vol. 68, no. 2, pp. 592-605, 2021. paper
  4. Kohei Nakai, Takashi Matsubara, Kuniaki Uehara, "Neural Architecture Search for Convolutional Neural Networks with Attention," IEICE Transactions on Information and Systems, vol. E104.D, no. 2, pp. 312-321, 2021. paper
  5. Takashi Matsubara, "Target-Oriented Deformation of Visual-Semantic Embedding Space," IEICE Transactions on Information and Systems, vol. E104.D, no. 1, pp. 24-33, 2021. paper arXiv

国際会議論文

  1. Takashi Matsubara, Yuto Miyatake, Takaharu Yaguchi, "Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory," Advances in Neural Information Processing Systems 34 (NeurIPS2021), Virtual, Dec. 2021. paper arXiv
  2. Yuhan Chen, Takashi Matsubara, Takaharu Yaguchi, "Neural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems," Advances in Neural Information Processing Systems 34 (NeurIPS2021), Virtual, Dec. 2021. (spotlight) paper
  3. Takumi Kimura, Takashi Matsubara, Kuniaki Uehara, "ChartPointFlow for Topology-Aware 3D Point Cloud Generation," ACM International Conference on Multimedia (ACMMM2021), Virtual, Oct. 2021. (oral) paper arXiv

ワークショップ

  1. Rousslan Fernand Julien Dossa, Takashi Matsubara, "Toward Human Cognition-inspired High-Level Decision Making For Hierarchical Reinforcement Learning Agents," The 2021 Nonlinear Science Workshop (NLSW2021), Virtual, Dec. 2021.
  2. Takehiro Aoshima, Takashi Matsubara, Takaharu Yaguchi, "Deep Discrete-Time Lagrangian Mechanics," ICLR2021 Workshop on Deep Learning for Simulation (SimDL), Virtual, May, 2021. link

2020

原著論文

  1. Rousslan Fernand Julien Dossa, Xinyu Lian, Hirokazu Nomoto, Takashi Matsubara, Kuniaki Uehara, "Hybrid of Reinforcement and Imitation Learning for Human-Like Agents," IEICE Transactions on Information and Systems, vol. E103.D, no. 9, pp. 1960-1970, 2020. paper
  2. Kazuki Kawamura, Takashi Matsubara, Kuniaki Uehara, "Deep State-Space Model for Noise Tolerant Skeleton-based Action Recognition," IEICE Transactions on Information and Systems, vol. E103.D, no. 6, pp. 1217-1225, 2020. paper
  3. Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara, "Data Augmentation using Random Image Cropping and Patching for Deep CNNs," IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 9, pp. 2917-2931, 2020. paper arXiv
  4. Takashi Matsubara, "Bayesian Deep Learning: A Model-based Interpretable Approach," Nonlinear Theory and Its Applications, IEICE, vol. E11-N, no. 1, pp. 16-35, 2020. (invited) paper

国際会議論文

  1. Takashi Matsubara, Ai Ishikawa, Takaharu Yaguchi, "Deep Energy-Based Modeling of Discrete-Time Physics," Advances in Neural Information Processing Systems 33 (NeurIPS2020), Virtual, Dec. 2020. (oral) paper arXiv
  2. Kohei Nakai, Takashi Matsubara, Kuniaki Uehara, "Att-DARTS: Differentiable Neural Architecture Search for Attention," The 2020 International Joint Conference on Neural Networks (IJCNN2020), Glasgow (Virtual), Jul. 2020. paper

ワークショップ

  1. Shunpei Terakawa, Takashi Matsubara, Takaharu Yaguchi, "The Error Analysis of Numerical Integrators for Deep Neural Network Modeling of Differential Equations," NeurIPS2020 Workshop on Machine Learning and the Physical Sciences (ML4PS), Virtual, Dec. 2020. link
  2. Boqian Zhou, Hirokazu Nomoto, Takashi Matsubara, Kuniaki Uehara, "Training Pedestrians' Detector Based on Hybrid Loss with Weak Annotations," The 8th Korea-Japan Joint Workshop on Complex Communication Sciences (KJCCS), Hiroshima, Jan. 2020.

2019

原著論文

  1. Kohei Shimamura, Shogo Fukushima, Akihide Koura, Fuyuki Shimojo, Masaaki Misawa, Rajiv Kalia, Aiichiro Nakano, Priya Vashishta, Takashi Matsubara, and Shigenori Tanaka, "Guidelines for Creating Artificial Neural Network Empirical Interatomic Potential from First-Principles Molecular Dynamics Data under Specific Conditions and Its Application to α-Ag₂Se," Journal of Chemical Physics, vol.151, 124303, 2019. paper
  2. Makoto Naruse, Takashi Matsubara, Nicolas Chauvet, Kazutaka Kanno, Tianyu Yang, and Atsushi Uchida, "Generative adversarial network based on chaotic time series," Scientific Reports, vol. 9, Article no. 12963, 2019. paper
  3. Takashi Matsubara, Tetsuo Tashiro, and Kuniaki Uehara, "Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis," IEEE Transactions on Biomedical Engineering, vol. 66, no. 10, pp. 2768-2779, 2019. paper arXiv
  4. Ryo Takahashi, Takashi Matsubara, and Kuniaki Uehara, "A Novel Weight-Shared Multi-Stage CNN for Scale Robustness," IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 4, pp. 1090-1101, 2019. paper arXiv
  5. Kenya Ukai, Takashi Matsubara, and Kuniaki Uehara, "Bayesian Estimation and Model Averaging of Convolutional Neural Networks by Hypernetwork," Nonlinear Theory and Its Applications, IEICE, Vol.E10-N, No.1, 2019. paper

国際会議論文

  1. Kazuki Sato, Kenta Hama, Takashi Matsubara, and Kuniaki Uehara, "Predictable Uncertainty-Aware Unsupervised Deep Anomaly Segmentation," The 2019 International Joint Conference on Neural Networks (IJCNN2019), Budapest, Jul. 2019. paper
  2. Koki Kusano, Tetsuo Tashiro, Takashi Matsubara, and Kuniaki Uehara, "Deep Generative State-Space Modeling of FMRI Images for Psychiatric Disorder Diagnosis," The 2019 International Joint Conference on Neural Networks (IJCNN2019), Budapest, Jul. 2019. paper
  3. Rousslan Fernand Julien Dossa, Xinyu Lian, Hirokazu Nomoto, Takashi Matsubara, and Kuniaki Uehara, "A Human-Like Agent Based on a Hybrid of Reinforcement and Imitation Learning," The 2019 International Joint Conference on Neural Networks (IJCNN2019), Budapest, Jul. 2019. paper

ワークショップ

  1. Kenta Hama, Takashi Matsubara, and Kuniaki Uehara, "Image-Caption Retrieval with Evaluating Uncertainties," The 7th Japan-Korea Joint Workshop on Complex Communication Sciences (JKCCS), Pyengonchang, Jan. 2019. (Best Paper Award)

2018

原著論文

  1. Takashi Matsubara, Ryo Akita, and Kuniaki Uehara, "Stock Price Prediction by Deep Neural Generative Model of News Articles," IEICE Transactions on Information and Systems, Vol.E101-D, No.4, pp.901-908, 2018. paper
  2. Takashi Matsubara and Kuniaki Uehara, "Asynchronous Network of Cellular Automaton-based Neurons for Efficient Implementation of Boltzmann Machines," Nonlinear Theory and Its Applications, IEICE, vol. E9-N, No.1, pp. 24-35, 2018. paper
  3. Hiroaki Mano, Gopal Kotecha, Kenji Leibnitz, Takashi Matsubara, Aya Nakae, Nicholas Shenker, Masahiko Shibata, Valerie Voon, Wako Yoshida, Michael Lee, Toshio Yanagida, Mitsuo Kawato, Maria Joao Rosa, and Ben Seymour, "Classification and characterisation of brain network changes in chronic back pain: A multicenter study," Wellcome Open Research, vol. 3, no. 19, 2018. paper

国際会議論文

  1. Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara, "RICAP: Random Image Cropping and Patching Data Augmentation for Deep CNNs," The 10th Asian Conference on Machine Learning (ACML2018), Beijing, Nov. 2018, pp. 786-798. paper
  2. Kenya Ukai, Takashi Matsubara, Kuniaki Uehara, "Hypernetwork-based Implicit Posterior Estimation and Model Averaging of Convolutional Neural Networks," The 10th Asian Conference on Machine Learning (ACML2018), Beijing, Nov. 2018, pp. 176-191. paper
  3. Takashi Matsubara, Tetsuo Tashiro, Kuniaki Uehara, "Structured Deep Generative Model of FMRI Signals for Mental Disorder Diagnosis," The 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2018), Granada, Sep. 2018, pp. 258-266. paper
  4. Takashi Matsubara, Ryosuke Tachibana, Kuniaki Uehara, "Anomaly Machine Component Detection by Deep Generative Model with Unregularized Score," The 2018 International Joint Conference on Neural Networks (IJCNN2018), Rio de Janeiro, Jul. 2018, pp. 4067-4074. paper

ワークショップ

  1. Xiao Zeng, Takashi Matsubara, Kuniaki Uehara, "Episode-efficient Exploration for Safe Reinforcement Learning," The 2018 International Symposium on Nonlinear Theory and its Applications (NOLTA2018), Tarragon.

2017

原著論文

  1. Takashi Matsubara, "Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns," Frontiers in Computational Neuroscience, 21 Nov. 2017. paper
  2. Yusuke Kataoka, Takashi Matsubara, and Kuniaki Uehara, "Deep Manga Colorization with Color Style Extraction by Conditional Adversarially Learned Inference," IEE: Information Engineering Express, vol. 3, no. 4, pp. 55-66, 2017.
  3. Ryosuke Tachibana, Takashi Matsubara, and Kuniaki Uehara, "Auto-encoder with Adversarially Regularized Latent Variables for Semi-Supervised Learning," IEE: Information Engineering Express, vol. 3, no. 3, pp. 11-20, 2017.

国際会議論文

  1. Ryo Takahashi, Takashi Matsubara, and Kuniaki Uehara, "Scale-Invariant Recognition by Weight-Shared CNNs in Parallel," The 9th Asian Conference on Machine Learning (ACML 2017), Seoul, Nov. 2017. paper
  2. Yuusuke Kataoka, Takashi Matsubara, and Kuniaki Uehara, "Automatic Manga Colorization with Color Style by Generative Adversarial Nets," The 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2017), Kanazawa, Jun. 2017, SS2-08. paper
  3. Shohei Miyashita, Xinyu Lian, Xiao Zeng, Takashi Matsubara, and Kuniaki Uehara, "Developing Game AI Agent Behaving Like Human by Mixing Reinforcement Learning and Supervised Learning," The 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2017), Kanazawa, Jun. 2017, SS2-07. paper
  4. Takashi Matsubara, "Spike Timing-Dependent Conduction Delay Learning Model Classifying Spatio-Temporal Spike Patterns," The 2017 International Joint Conference on Neural Networks (IJCNN2017), Anchorage, May 2017, 164. paper

ワークショップ

  1. Takashi Matsubara and Kuniaki Uehara, "Efficient Implementation of Boltzmann Machine using Asynchronous Network of Cellular Automaton-based Neurons," The 2016 International Symposium on Nonlinear Theory and its Applications (NOLTA2016), Yugawara, Nov. 2016, pp. 634-637.
  2. Takashi Matsubara and Kuniaki Uehara, "The STDP with Fluctuations Agrees with the Changes and the Distributions of the Synaptic Weights," The 2015 International Symposium on Nonlinear Theory and its Applications (NOLTA2015), Hong Kong, Dec. 2015, pp. 217-220.
  3. Takashi Matsubara and Hiroyuki Torikai, "Long-Term Spine Volume Dynamics Corresponds Partially With Multiplicative STDP," The 2014 International Symposium on Nonlinear Theory and its Applications (NOLTA2014), Luzern, Sep. 2014, pp. 699-702.
  4. Takashi Matsubara, Hiroyuki Torikai, Tetsuya Shimokawa, Kenji Leibnitz, and Ferdinand Peper, "A Nonlinear Circuit Network Toward Brain Voxel Modeling," The 2013 International Symposium on Nonlinear Theory and its Applications (NOLTA2013), Santa Fe, Sep. 2013, pp. 421-424.
  5. Takashi Matsubara and Hiroyuki Torikai, "Basic Analysis of Generalized Asynchronous Digital Spiking Neuron Model," The 2011 International Symposium on Nonlinear Theory and its Applications (NOLTA2011), Kobe, Sep. 2011, pp. 60-63.

2016

原著論文

  1. Ryo Akita, Akira Yoshihara, Takashi Matsubara, and Kuniaki Uehara, "Deep Learning for Stock Prediction Using Numerical and Textual Information," International Journal of Computer & Information Science, vol. 17, pp. 11-16, 2016.
  2. Takashi Matsubara and Kuniaki Uehara, "Homeostatic Plasticity Achieved by Incorporation of Random Fluctuations and Soft-Bounded Hebbian Plasticity in Excitatory Synapses," Frontiers in Neural Circuits, vol. 10, no. 42, 2016. paper
  3. Takashi Matsubara and Hiroyuki Torikai, "An Asynchronous Recurrent Network of Cellular Automaton-based Neurons and its Reproduction of Spiking Neural Network Activities," IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 4, pp.836-852, 2016. paper

国際会議論文

  1. Takashi Matsubara and Kuniaki Uehara, "A Novel Homeostatic Plasticity Model Realized by Random Fluctuations in Excitatory Synapses," The 2016 International Joint Conference on Neural Networks (IJCNN2016), Vancouver, Jul. 2016, N-16352.
  2. Ryo Akita, Akira Yoshihara, Takashi Matsubara, and Kuniaki Uehara, "Deep Learning for Stock Prediction Using Numerical and Textual Information," the 15th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2016), Okayama, Jun. 2016, pp. 945-950. paper
  3. Ryosuke Tachibana, Takashi Matsubara, and Kuniaki Uehara, "Semi-Supervised Learning Using Adversarial Networks," the 15th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2016), Okayama, Jun. 2016, pp. 939-944. paper
  4. Yuusuke Kataoka, Takashi Matsubara, and Kuniaki Uehara, "Image Generation Using Generative Adversarial Networks and Attention Mechanism," the 15th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2016), Okayama, Jun. 2016, pp. 933-938. paper

ワークショップ

  1. Takashi Matsubara and Kuniaki Uehara, "Efficient Implementation of Boltzmann Machine using Asynchronous Network of Cellular Automaton-based Neurons," The 2016 International Symposium on Nonlinear Theory and its Applications (NOLTA2016), Yugawara, Nov. 2016, pp. 634-637.

2015

原著論文

  1. Takashi Matsubara, Hiroyuki Torikai, Tetsuya Shimokawa, Kenji Leibnitz, and Ferdinand Peper, "A Novel Double Oscillation Model for Prediction of fMRI BOLD Signals without Detrending," IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol.E98.A, no.9, pp.1924-1936, 2015. paper

ワークショップ

  1. Takashi Matsubara and Kuniaki Uehara, "The STDP with Fluctuations Agrees with the Changes and the Distributions of the Synaptic Weights," The 2015 International Symposium on Nonlinear Theory and its Applications (NOLTA2015), Hong Kong, Dec. 2015, pp. 217-220.

2014

国際会議論文

  1. Takashi Matsubara, Hiroyuki Torikai, Tetsuya Shimokawa, Kenji Leibnitz, and Ferdinand Peper, "A Nonlinear Model of fMRI BOLD Signal Including the Trend Component," The 2014 International Joint Conference on Neural Networks (IJCNN2014), Beijing, Jul. 2014, pp. 2579-2586. paper

ワークショップ

  1. Takashi Matsubara and Hiroyuki Torikai, "Long-Term Spine Volume Dynamics Corresponds Partially With Multiplicative STDP," The 2014 International Symposium on Nonlinear Theory and its Applications (NOLTA2014), Luzern, Sep. 2014, pp. 699-702.

2013

原著論文

  1. Takashi Matsubara and Hiroyuki Torikai, "Asynchronous Cellular Automaton-Based Neuron: Theoretical Analysis and On-FPGA Learning," IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 5, pp. 736-748, 2013. paper
  2. Takashi Matsubara and Hiroyuki Torikai, "Bifurcation-based Synthesis of Asynchronous Cellular Automaton Based Neuron," Nonlinear Theory and Its Applications, IEICE, vol. 4, no. 1, pp. 111-126, 2013. paper

国際会議論文

  1. Takashi Matsubara and Hiroyuki Torikai, "A Novel Reservoir Network of Asynchronous Cellular Automaton based Neurons for MIMO Neural System Reproduction," The 2013 International Joint Conference on Neural Networks (IJCNN2013), 1585, Dallas, Aug. 2013, pp. 1563-1569. paper

ワークショップ

  1. Takashi Matsubara, Hiroyuki Torikai, Tetsuya Shimokawa, Kenji Leibnitz, and Ferdinand Peper, "A Nonlinear Circuit Network Toward Brain Voxel Modeling," The 2013 International Symposium on Nonlinear Theory and its Applications (NOLTA2013), Santa Fe, Sep. 2013, pp. 421-424.

2012

原著論文

  1. Takashi Matsubara and Hiroyuki Torikai, "Neuron-Like Responses and Bifurcations of a Generalized Asynchronous Sequential Logic Spiking Neuron Model," IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E95.A, no. 8, pp. 1317-1328, 2012. paper

国際会議論文

  1. Takashi Matsubara and Hiroyuki Torikai, "A Novel Bifurcation-based Synthesis of Asynchronous Cellular Automaton Based Neuron," Artificial Neural Networks and Machine Learning - ICANN 2012 (International Conference on Artificial Neural Networks), ser. Lecture Notes in Computer Science, vol. 7552, Lausanne, Sep. 2012, pp. 231-238. paper
  2. Hiroyuki Torikai and Takashi Matsubara, "Asynchronous Cellular Automaton Based Modeling of Nonlinear Dynamics of Neuron," International Conference on Theory and Application in Nonlinear Dynamics (ICAND 2012), ser. Understanding Complex Systems, Seattle, Aug. 2012, pp. 101-112. paper
  3. Takashi Matsubara and Hiroyuki Torikai, "A Generalized Asynchronous Digital Spiking Neuron: Theoretical Analysis and Compartmental Model," The 2012 International Joint Conference on Neural Networks (IJCNN2012), Brisbane, Jun. 2012, pp. 185-192. paper

2011

原著論文

  1. Takashi Matsubara, Hiroyuki Torikai, and Tetsuya Hishiki, "A Generalized Rotate-and-Fire Digital Spiking Neuron Model and Its On-FPGA Learning," IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 58, no. 10, pp. 677-681, 2011. paper

国際会議論文

  1. Takashi Matsubara and Hiroyuki Torikai, "Dynamic Response Behaviors of a Generalized Asynchronous Digital Spiking Neuron Model," Neural Information Processing - 18th International Conference (International Conference on Neural Information Processing), ser. Lecture Notes in Computer Science (ICONIP2011), Shanghai, vol. 7064, no. III, Nov. 2011, pp. 395-404. paper
  2. Takashi Matsubara and Hiroyuki Torikai, "A Novel Asynchronous Digital Spiking Neuron Model and its Various Neuron-like Bifurcations and Responses," The 2011 International Joint Conference on Neural Networks (IJCNN2011), San Jose, Aug. 2011, pp. 741-748. paper

ワークショップ

  1. Takashi Matsubara and Hiroyuki Torikai, "Basic Analysis of Generalized Asynchronous Digital Spiking Neuron Model," The 2011 International Symposium on Nonlinear Theory and its Applications (NOLTA2011), Kobe, Sep. 2011, pp. 60-63.