Yoshinari Takeishi (武石 啓成)

Assistant Professor, Kyushu University
Faculty of Information Science and Electrical Engineering

Mathematical Engineering Laboratory

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九州大学研究者情報

Publication List

Journal Papers

  1. Y. Takeishi, M. Iida & J. Takeuchi:
    “Approximate Spectral Decomposition of Fisher Information Matrix for Simple ReLU Networks,” Neural Networks, Vol. 164, pp. 691-706, July 2023. (Link)
  2. Y. Takeishi & J. Takeuchi:
    “An Improved Analysis of Least Squares Superposition Codes with Bernoulli Dictionary,” Japanese Journal of Statistics and Data Science, 2, pp. 591-613, September 2019.
  3. Y. Takeishi, M. Kawakita, & J. Takeuchi:
    “Least Squares Superposition Codes with Bernoulli Dictionary are Still Reliable at Rates up to Capacity,” IEEE Transactions on Information Theory, Vol. 60, No. 5, pp. 2737-2750, May 2014.

Conference Papers (peer reviewed)

  1. M. Iida, Y. Takeishi, & J. Takeuchi:
    “On Fisher Information Matrix for Simple Neural Networks With Softplus Activation,” Proc. of 2022 IEEE International Symposium on Information Theory, pp. 3014 - 3019, Espoo, Finland, June 26-July 1, 2022.
  2. Y. Takeishi & J. Takeuchi:
    “An Improved Upper Bound on Block Error Probability of Least Squares Superposition Codes with Unbiased Bernoulli Dictionary,” Proc. of 2016 IEEE International Symposium on Information Theory, pp. 1168 - 1172, Barcelona, Spain, July 10-15, 2016.
  3. Y. Takeishi, M. Kawakita, & J. Takeuchi:
    “Least Squares Superposition Codes with Bernoulli Dictionary are Still Reliable at Rates up to Capacity,” Proc. of 2013 IEEE International Symposium on Information Theory, pp. 1396-1400, Istanbul, Turkey, July 7-12, 2013.

Preprint

  1. Y. Takeishi, M. Iida, & J. Takeuchi:
    “Approximate Spectral Decomposition of Fisher Information Matrix for Simple ReLU Networks,” arXiv:2111.15256, 2021.

Materials

  1. Eigenvectors of Fisher Information Matrix for Simple ReLU Networks (IBIS2021, 2021/11/10~2021/11/13) (Link)
  2. On Sparse Superposition Codes and Discretization of Their Dictionaries (Mathematics for Innovation in Telecommunications Technology, 2022/09/15~2022/09/16) (Link)
  3. A novel method to stabilize gradient descent learning (MIRAI2.0 Research & Innovation Week 2022, 2022/11/16) (Link)

Research Interest

Curriculum Vitae

Education

Job Experience

Contact

takeishi(at)inf.kyushu-u.ac.jp