Journal Articles

  1. 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. (link) (arXiv)
  2. Takumi Kimura, Takashi Matsubara, and Kuniaki Uehara, “Topology-Aware Flow-Based Point Cloud Generation,” IEEE Transactions on Circuits and Systems for Video Technology, 2022. (accepted) (link)
  3. 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.
  4. Rousslan Fernand Julien Dossa, Shengyi Huang, Santiago Ontañón, and Takashi Matsubara, “An Empirical Investigation of Early Stopping Optimizations in Proximal Policy Optimization,” IEEE Access, vol. 9, pp. 117981-117992, 2021. (link)
  5. Kenta Hama, Takashi Matsubara, Kuniaki Uehara, and 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. (link) (arXiv)
  6. Takashi Matsubara, Koki Kusano, Tetsuo Tashiro, Ken’ya Ukai, and 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. (link)
  7. Kohei Nakai, Takashi Matsubara, and 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. (link)
  8. 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. (link) (arXiv)
  9. Rousslan Fernand Julien Dossa, Xinyu Lian, Hirokazu Nomoto, Takashi Matsubara, and 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. (link)
  10. Kazuki Kawamura, Takashi Matsubara, and 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. (link)
  11. Ryo Takahashi, Takashi Matsubara, and 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. (link) (arXiv)
  12. 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). (link)
  13. 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. (link)
  14. 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. (link)
  15. 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. (link) (arXiv)
  16. 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. (link) (arXiv)
  17. 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. (link)
  18. 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. (link)
  19. 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. (link)
  20. 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. (link)
  21. Takashi Matsubara, “Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns,” Frontiers in Computational Neuroscience, 21 Nov. 2017. (link)
  22. 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.
  23. 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.
  24. 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.
  25. 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. (link)
  26. 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. (link)
  27. 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. (link)
  28. 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. (link)
  29. 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. (link)
  30. 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. (link)
  31. 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. (link)

International Conference (Peer-Reviewed)

  1. 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)
  2. Yuhan Chen, Takashi Matsubara, and Takaharu Yaguchi, “KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-Zero Training Loss,” Proc. of The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI2022), Virtual, Feb., 2022. (arXiv)
  3. Takashi Matsubara, Yuto Miyatake, and 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. (acceptance rate 26%) (link) (arXiv)
  4. Yuhan Chen, Takashi Matsubara, and Takaharu Yaguchi, “Neural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems,” Advances in Neural Information Processing Systems 34 (NeurIPS2021), Virtual, Dec., 2021. (spotlight 3%) (link)
  5. Rousslan Fernand Julien Dossa and Takashi Matsubara, “Toward Human Cognition-inspired High-Level Decision Making For Hierarchical Reinforcement Learning Agents,” The 2021 Nonlinear Science Workshop (NLSW2021), Virtual, Dec., 2021.
  6. Takumi Kimura, Takashi Matsubara, and Kuniaki Uehara, “ChartPointFlow for Topology-Aware 3D Point Cloud Generation,” Proc. of ACM International Conference on Multimedia (ACMMM2021), Virtual, Oct., 2021. (oral 179/1,942=9.2%) (arXiv)
  7. Takehiro Aoshima, Takashi Matsubara, and Takaharu Yaguchi, “Deep Discrete-Time Lagrangian Mechanics,” ICLR2021 Workshop on Deep Learning for Simulation (SimDL), Virtual, May, 2021. (link)
  8. Shunpei Terakawa, Takashi Matsubara, and 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)
  9. Takashi Matsubara, Ai Ishikawa, and Takaharu Yaguchi, “Deep Energy-Based Modeling of Discrete-Time Physics,” Advances in Neural Information Processing Systems 33 (NeurIPS2020), Virtual, Dec., 2020. (oral 105/9,454=1.1%) (link) (arXiv)
  10. Kohei Nakai, Takashi Matsubara, and Kuniaki Uehara, “Att-DARTS: Differentiable Neural Architecture Search for Attention,” Proc. of The 2020 International Joint Conference on Neural Networks (IJCNN2020), Glasgow (Virtual), Jul. 2020. (link)
  11. Boqian Zhou, Hirokazu Nomoto, Takashi Matsubara, and Kuniaki Uehara, “Training Pedestrians’ Detector Based on Hybrid Loss with Weak Annotations,” Proc. of The 8th Korea-Japan Joint Workshop on Complex Communication Sciences (KJCCS), Hiroshima, Jan. 2020.
  12. Kazuki Sato, Kenta Hama, Takashi Matsubara, and Kuniaki Uehara, “Predictable Uncertainty-Aware Unsupervised Deep Anomaly Segmentation,” Proc. of The 2019 International Joint Conference on Neural Networks (IJCNN2019), Budapest, Jul. 2019. (link)
  13. Koki Kusano, Tetsuo Tashiro, Takashi Matsubara, and Kuniaki Uehara, “Deep Generative State-Space Modeling of FMRI Images for Psychiatric Disorder Diagnosis,” Proc. of The 2019 International Joint Conference on Neural Networks (IJCNN2019), Budapest, Jul. 2019. (link)
  14. 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,” Proc. of The 2019 International Joint Conference on Neural Networks (IJCNN2019), Budapest, Jul. 2019. (link)
  15. Kenta Hama, Takashi Matsubara, and Kuniaki Uehara, “Image-Caption Retrieval with Evaluating Uncertainties,” Proc. of The 7th Japan-Korea Joint Workshop on Complex Communication Sciences (JKCCS), Pyengonchang, Jan. 2019.
  16. Ryo Takahashi, Takashi Matsubara, and Kuniaki Uehara, “RICAP: Random Image Cropping and Patching Data Augmentation for Deep CNNs,” Proc. of The 10th Asian Conference on Machine Learning (ACML2018), Beijing, Nov. 2018, pp. 786-798. (acceptance rate 57/230=0.248) (link)
  17. Kenya Ukai, Takashi Matsubara, and Kuniaki Uehara, “Hypernetwork-based Implicit Posterior Estimation and Model Averaging of Convolutional Neural Networks,” Proc. of The 10th Asian Conference on Machine Learning (ACML2018), Beijing, Nov. 2018, pp. 176-191. (acceptance rate 57/230=0.248) (link)
  18. Takashi Matsubara, Tetsuo Tashiro, and Kuniaki Uehara, “Structured Deep Generative Model of FMRI Signals for Mental Disorder Diagnosis,” Proc. of The 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2018), Granada, Sep. 2018, pp. 258-266. (acceptance rate 372/1,068=0.348) (link)
  19. Xiao Zeng, Takashi Matsubara, and Kuniaki Uehara, “Episode-efficient Exploration for Safe Reinforcement Learning,” Proc. of The 2018 International Symposium on Nonlinear Theory and its Applications (NOLTA2018), Tarragona, Sep. 2018.
  20. Takashi Matsubara, Ryosuke Tachibana, and Kuniaki Uehara, “Anomaly Machine Component Detection by Deep Generative Model with Unregularized Score,” Proc. of The 2018 International Joint Conference on Neural Networks (IJCNN2018), Rio de Janeiro, Jul. 2018, pp.4067-4074. (link)
  21. Tetsuo Tashiro, Takashi Matsubara, and Kuniaki Uehara, “Deep Neural Generative Model for fMRI Image Based Diagnosis of Mental Disorder,” Proc. of The 2017 International Symposium on Nonlinear Theory and its Applications (NOLTA2017), Cancun, Dec. 2017, pp. 700-703, 5169.
  22. Ryo Takahashi, Takashi Matsubara, and Kuniaki Uehara, “Multi-Stage Convolutional Neural Networks for Robustness to Scale Transformation,” Proc. of The 2017 International Symposium on Nonlinear Theory and its Applications (NOLTA2017), Cancun, Dec. 2017, pp. 692-695, 5056.
  23. Ryo Takahashi, Takashi Matsubara, and Kuniaki Uehara, “Scale-Invariant Recognition by Weight-Shared CNNs in Parallel,” Proc. of The 9th Asian Conference on Machine Learning (ACML 2017), Seoul, Nov. 2017. (acceptance rate 41/172=0.238) (link).
  24. Yuusuke Kataoka, Takashi Matsubara, and Kuniaki Uehara, “Automatic Manga Colorization with Color Style by Generative Adversarial Nets,” Proc. of The 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2017), Kanazawa, Jun. 2017, SS2-08. (link)
  25. 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,” Proc. of The 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2017), Kanazawa, Jun. 2017, SS2-07. (link)
  26. Takashi Matsubara, “Spike Timing-Dependent Conduction Delay Learning Model Classifying Spatio-Temporal Spike Patterns,” Proc. of The 2017 International Joint Conference on Neural Networks (IJCNN2017), Anchorage, May 2017, 164. (link)
  27. Takashi Matsubara and Kuniaki Uehara, “Efficient Implementation of Boltzmann Machine using Asynchronous Network of Cellular Automaton-based Neurons,” Proc. of The 2016 International Symposium on Nonlinear Theory and its Applications (NOLTA2016), Yugawara, Nov. 2016, pp. 634-637.
  28. Takashi Matsubara and Kuniaki Uehara, “A Novel Homeostatic Plasticity Model Realized by Random Fluctuations in Excitatory Synapses,” Proc. of The 2016 International Joint Conference on Neural Networks (IJCNN2016), Vancouver, Jul. 2016, N-16352.
  29. Ryo Akita, Akira Yoshihara, Takashi Matsubara, and Kuniaki Uehara, “Deep Learning for Stock Prediction Using Numerical and Textual Information,” Proc. of the 15th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2016), Okayama, Jun. 2016, pp. 945-950. (link)
  30. Ryosuke Tachibana, Takashi Matsubara, and Kuniaki Uehara, “Semi-Supervised Learning Using Adversarial Networks,” Proc. of the 15th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2016), Okayama, Jun. 2016, pp. 939-944. (link)
  31. Yuusuke Kataoka, Takashi Matsubara, and Kuniaki Uehara, “Image Generation Using Generative Adversarial Networks and Attention Mechanism,” Proc. of the 15th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2016), Okayama, Jun. 2016, pp. 933-938. (link)
  32. Takashi Matsubara and Kuniaki Uehara, “The STDP with Fluctuations Agrees with the Changes and the Distributions of the Synaptic Weights,” in Proc. of The 2015 International Symposium on Nonlinear Theory and its Applications (NOLTA2015), Hong Kong, Dec. 2015, pp. 217-220.
  33. Takashi Matsubara and Hiroyuki Torikai, “Long-Term Spine Volume Dynamics Corresponds Partially With Multiplicative STDP,” in Proc. of The 2014 International Symposium on Nonlinear Theory and its Applications (NOLTA2014), Luzern, Sep. 2014, pp. 699-702.
  34. Takashi Matsubara, Hiroyuki Torikai, Tetsuya Shimokawa, Kenji Leibnitz, and Ferdinand Peper, “A Nonlinear Model of fMRI BOLD Signal Including the Trend Component,” Proc. of The 2014 International Joint Conference on Neural Networks (IJCNN2014), Beijing, Jul. 2014, pp. 2579-2586. (link)
  35. Takashi Matsubara, Hiroyuki Torikai, Tetsuya Shimokawa, Kenji Leibnitz, and Ferdinand Peper, “A Nonlinear Circuit Network Toward Brain Voxel Modeling,” in Proc. of The 2013 International Symposium on Nonlinear Theory and its Applications (NOLTA2013), Santa Fe, Sep. 2013, pp. 421-424.
  36. Takashi Matsubara and Hiroyuki Torikai, “A Novel Reservoir Network of Asynchronous Cellular Automaton based Neurons for MIMO Neural System Reproduction,” in Proc. of The 2013 International Joint Conference on Neural Networks (IJCNN2013), 1585, Dallas, Aug. 2013, pp. 1563-1569. (link)
  37. Takashi Matsubara and Hiroyuki Torikai, “A Novel Bifurcation-based Synthesis of Asynchronous Cellular Automaton Based Neuron,” in Artificial Neural Networks and Machine Learning - ICANN 2012 (Proc. of International Conference on Artificial Neural Networks), ser. Lecture Notes in Computer Science, vol. 7552, Lausanne, Sep. 2012, pp. 231-238. (link)
  38. Hiroyuki Torikai and Takashi Matsubara, “Asynchronous Cellular Automaton Based Modeling of Nonlinear Dynamics of Neuron,” in International Conference on Theory and Application in Nonlinear Dynamics (ICAND 2012), ser. Understanding Complex Systems, Seattle, Aug. 2012, pp. 101-112. (link)
  39. Takashi Matsubara and Hiroyuki Torikai, “A Generalized Asynchronous Digital Spiking Neuron: Theoretical Analysis and Compartmental Model,” in Proc. of The 2012 International Joint Conference on Neural Networks (IJCNN2012), Brisbane, Jun. 2012, pp. 185-192. (link)
  40. Takashi Matsubara and Hiroyuki Torikai, “Dynamic Response Behaviors of a Generalized Asynchronous Digital Spiking Neuron Model,” in Neural Information Processing - 18th International Conference (Proc. of International Conference on Neural Information Processing), ser. Lecture Notes in Computer Science (ICONIP2011), Shanghai, vol. 7064, no. III, Nov. 2011, pp. 395-404. (link)
  41. Takashi Matsubara and Hiroyuki Torikai, “Basic Analysis of Generalized Asynchronous Digital Spiking Neuron Model,” in Proc. of The 2011 International Symposium on Nonlinear Theory and its Applications (NOLTA2011), Kobe, Sep. 2011, pp. 60-63.
  42. Takashi Matsubara and Hiroyuki Torikai, “A Novel Asynchronous Digital Spiking Neuron Model and its Various Neuron-like Bifurcations and Responses,” in Proc. of The 2011 International Joint Conference on Neural Networks (IJCNN2011), San Jose, Aug. 2011, pp. 741-748. (link)

International Symposium

  1. Kimiaki Shirahama, Takumi. Sato, Norihiro Yamawaki, Takashi Matsubara, and Kuniaki Uehara, “Kindai University and Osaka Gakuin University and Osaka University at TRECVID 2021 AVS Tasks,” Proc. of TREC Video Retrieval Evaluation (TRECVID), Virtual, Nov. 2021.
  2. Daiki Mukai, Ryosuke Utsunomiya, Shunsuke Utsuki, Kimiaki Shirahama, Takashi Matsubara, and Kuniaki Uehara, “Kindai University and Osaka Gakuin University at TRECVID 2020 AVS and ActEV Tasks,” Proc. of TREC Video Retrieval Evaluation (TRECVID), Virtual, Nov. 2020.
  3. Kimiaki Shirahama, Daichi Sakurai, Takashi Matsubara, and Kuniaki Uehara, “Kindai University and Kobe University at TRECVID 2019 AVS Task,” Proc. of TREC Video Retrieval Evaluation (TRECVID), Gaithersburg, Nov. 2019.
  4. Takashi Matsubara “Neural Generative Model of Small Dataset for Leveraging Our Knowledge,” in The 2nd NTU-Kobe U Joint Workshop 2018, Data Science and Artificial Intelligence, Singapore, Mar. 2018.
  5. Takashi Matsubara “Artificial Neural Networks with Domain-Knowledge,” in The 7th Kobe University Brussels European Center Symposium, Brussels, Nov. 2016.
  6. Takashi Matsubara and H. Torikai, “Asynchronous Cellular Automaton Based Neuron and its Reproduction Capability of Neuron-like Responses,” in Kyoto Workshop on NOLTA, Kyoto, Nov. 2011, NP07.

Book Chapters

  1. Takashi Matsubara and Hiroyuki Torikai, “Hardware-oriented neuron modeling approach by reconfigurable asynchronous cellular automaton”, in Mathematical Approaches to Biological Systems: Networks, Oscillations and Collective Motions, Springer (ed. T. Ohira), 2015, pp. 55-75. (link)

Invited Talks

  1. Takashi Matsubara, “Deep Learning Regularized by Structure and Hierarchy,” Proc. of The 7th Japan-Korea Joint Workshop on Complex Communication Sciences (JKCCS), Pyengonchang, Jan. 2019.