Yoshinari Takeishi (武石 啓成)

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.

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)

• Information theory (Coding theory, MDL principle)
  スパース重ね合わせ符号についての 解説論文 を書きました(2023年7月)
• Machine learning theory (Neural networks)

Education

• 2022 Dr.Eng.
  Graduate School of Information Science and Electrical Engineering, Kyushu University, Japan.
  Thesis: Some Studies on Linear Regression Problems in Sparse Superposition Codes and Neural Networks.
Advisor: Prof. Jun'ichi Takeuchi.
• 2013 M.Eng.
  Graduate School of Information Science and Electrical Engineering, Kyushu University, Japan.
  Thesis: A Study on Discrete Sparse Superposition Codes for Additive White Gaussian Noise Channel.
  Advisor: Prof. Jun'ichi Takeuchi.
• 2011 B.Eng.
  School of Engineering, Kyushu University, Japan.

Job Experience

• April 2022--present.
  Assistant Professor, Faculty of Information Science and Electrical Engineering, Kyushu University.
• April 2013--February 2021.
  Server Engineer at Mitsubishi Electric Information Network Corporation.

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