Talks

2026

  1. T. Furuya, “Approximation Theory for Lipschitz Continuous Transformers”, ICML 2026, Poster, Seoul, South Korea, July 2026.

  2. 古屋貴士, “反応拡散程式の作用素に対するニューラル作用素近似の理論誤差解析”, 第31回計算工学講演会, ウインクあいち, June 2026.

  3. 古屋貴士, “測度論的トランスフォーマーの理論解析”, RIMS共同研究「偏機械学習の数理と応用」, 京都大学数理解析研究所, May 2026.

  4. T. Furuya, “Mathematical Analysis of Measure Theoretic Transformers”, Applied and Numerical Analysis Seminar, Department of Mathematics, Chinese University of Hong Kong, May 2026.

  5. T. Furuya, “Mathematical Analysis of Measure-Theoretic Transformers”, The 10th Young Scholar Symposium of the East Asia Section of Inverse Problems International Association, Taiwan, April 2026. Best Presentation Award.

  6. T. Furuya, “Transformers Through the Lens of Support-preserving Maps Between Measures”, 2026 SIAM Conference on Uncertainty Quantification, Minneapolis, Minnesota, U.S., March 2026.

  7. T. Furuya, “Transformers are universal in-context learners”, Finland-Japan Workshop in Industrial and Applied Mathematics, Josai University, Tokyo, January 2026.

  8. T. Furuya, “Transformers are universal in-context learners”, RIMS共同研究「偏微分方程式に対する逆問題とその周辺」, 京都大学数理解析研究所, January 2026.

2025

  1. 古屋貴士, “Transformers are universal in-context learners”, 日本数学会 2025年度秋季総合分科会, 名古屋大学, September 2025.

  2. T. Furuya, “Transformers are universal in-context learners”, 2nd Workshop on Machine Learning in Infinite Dimensions, ETH Zürich, September 2025.

  3. T. Furuya, “Quantitative Approximation of Neural Operators for Semilinear elliptic PDEs”, The 44th International Conference on Simulation Technology, Aomori, Japan, September 2025.

  4. T. Furuya, “Transformers are universal in-context learners”, Deep Learning Theory Workshop 2025, Tokyo, Japan, August 2025.

  5. T. Furuya, “Transformers are universal in-context learners”, ICLR 2025, Poster, Singapore, April 2025.

  6. T. Furuya, “Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations”, ICLR 2025, Poster, Singapore, April 2025.

  7. T. Furuya, “Approximation Analysis for Neural Operators and Transformers in Applications to PDEs”, International Conference on Scientific Computing and Machine Learning 2025, Kyoto, Japan, March 2025.

  8. 古屋貴士, “Approximation Analysis for Neural Operators and Transformers in Applications to PDEs”, 関数空間論に基づく深層学習の理論と応用, 大阪大学, March 2025.

  9. 古屋貴士, “A tutorial on machine learning for the application to inverse problems”, RIMS共同研究「逆問題における数理解析と実践的研究」, 京都大学, January 2025.

2024

  1. T. Furuya, “Can neural operators always be continuously discretized?”, NeurIPS 2024, Poster, Vancouver, Canada, December 2024.

  2. T. Furuya, “Transformers are universal in-context learners”, Geo-Mathematical Imaging Group 2024 Project Review, Rice University, U.S., November 2024.

  3. T. Furuya, “Consistency of the Bayes method for the inverse scattering problem”, 2024 Japan-Taiwan Joint Workshop on Inverse Problems and Related Topics, Taipei, Taiwan, November 2024.

  4. 古屋貴士, “Transformers are universal in-context learners”, Poster, 第27回情報論的学習理論ワークショップ, さいたま, November 2024.

  5. 古屋貴士, “Theoretical perspectives for solving inverse problems with machine learning”, 若手研究集会「波動・振動・流れの制御と逆問題 -理論と数値計算-」, 京都大学, September 2024.

  6. T. Furuya, “Universal approximation for injective and bijective neural operators”, Poster, Finland-Japan Workshop in Industrial and Applied Mathematics, Helsinki, Finland, August 2024.

  7. T. Furuya, “Consistency of the Bayes method for the inverse scattering problem”, International Conference, Days On Diffraction, St. Petersburg, Russia, June 2024.

  8. 古屋貴士, “散乱逆問題に対するBernstein-von Mises定理とその数値実験について”, 神戸国際会議場, June 2024.

  9. 古屋貴士, “Globally injective and bijective neural operators”, 日本数学会 2024年度年会, 大阪公立大学, March 2024.

  10. 古屋貴士, “Neural operators for forward and inverse PDE problems”, 第15回 名古屋微分方程式研究集会, 名古屋大学, March 2024.

  11. 古屋貴士, “Globally injective and bijective neural operators”, RIMS共同研究「非適切問題に対する諸アプローチ-理論と実践-」, 京都大学数理解析研究所, January 2024.

2023

  1. T. Furuya, “Globally injective and bijective neural operators”, NeurIPS 2023, Poster, New Orleans, U.S., December 2023.

  2. 古屋貴士, “Globally injective and bijective neural operators”, 第3回数理解析若手交流会, December 2023.

  3. T. Furuya, “Mirror Descent for Minimax Problems in Adversarial Training”, The 11th Asian Conference on Fixed Point Theory and Optimization, Pattaya, Thailand, August 2023.

  4. 古屋貴士, “Fine-tuning neural-operator architectures for training and generalization”, ICIAN 2023, 早稲田大学, June 2023.

  5. 古屋貴士, “Fine-tuning neural-operator architectures for training and generalization”, 岡山応用数学セミナー, 岡山理科大学, June 2023.

  6. 古屋貴士, “カルマンフィルタによる散乱逆問題の再構成について”, 第28回計算工学講演会, つくば国際会議場, May 2023.

  7. T. Furuya, “Globally injective and bijective neural operators”, Geo-Mathematical Imaging Group 2023 Project Review, Rice University, U.S., May 2023.

  8. 古屋貴士, “Inverse medium scattering problems with Kalman filter techniques”, 日本数学会 2023年度年会, 中央大学, March 2023.

2022

  1. T. Furuya, “Local recovery of a piecewise constant anisotropic conductivity in EIT on domains with exposed corners”, Inverse Days 2022, Kuopio, Finland, December 2022.

  2. 古屋貴士, “Local recovery of a piecewise constant anisotropic conductivity in EIT on domains with exposed corners”, 若手研究集会「波動・振動・流れの制御と逆問題 -理論と数値計算-」, 愛媛大学, November 2022.

  3. T. Furuya, “Spectral Pruning for Recurrent Neural Networks”, Applied Harmonic Analysis and Machine Learning 2022, Genova, Italy, September 2022.

  4. T. Furuya, “Local recovery of a piecewise constant anisotropic conductivity in EIT on domains with exposed corners”, International Conference, Days On Diffraction, St. Petersburg, Russia, June 2022.

  5. T. Furuya, “Inverse medium scattering problems with Kalman filter techniques”, International Conference, Inverse Problems: Modeling and Simulation, Malta, May 2022.

  6. T. Furuya, “Spectral Pruning for Recurrent Neural Networks”, AISTATS 2022, Poster, March 2022.

  7. T. Furuya, “Scattering by the local perturbation of an open periodic waveguide in the half plane”, Conference on Mathematics of Wave Phenomena, Karlsruhe Institute of Technology, Germany, February 2022.

  8. T. Furuya, “The monotonicity method for the inverse crack scattering problem”, Conference on Mathematics of Wave Phenomena, Karlsruhe Institute of Technology, Germany, February 2022.

  9. 古屋貴士, “Inverse medium scattering problems with Kalman filter techniques”, RIMS共同研究, 京都大学数理解析研究所, January 2022.

2021

  1. 古屋貴士, “Scattering by the local perturbation of an open periodic waveguide in the half plane”, RIMS共同研究, 京都大学数理解析研究所, December 2021.

  2. 古屋貴士, “The direct and inverse scattering problem for the semilinear Schrödinger equation”, 日本数学会 2021年度秋季総合分科会, 千葉大学, September 2021.

  3. 古屋貴士, “Inverse medium scattering problems with Kalman filter techniques”, 若手研究集会「波動・振動・流れの制御と逆問題」, September 2021.

  4. 古屋貴士, “散乱逆問題に対する未知領域の再構成について”, 愛媛大学解析セミナー, 愛媛大学, July 2021.

  5. T. Furuya, “The monotonicity method for the inverse crack scattering problem”, Days On Diffraction, St. Petersburg, Russia, June 2021.

  6. 古屋貴士, “散乱逆問題に対する未知領域の再構成について”, 北海道大学偏微分方程式セミナー, 北海道大学, May 2021.

  7. 古屋貴士, “The monotonicity method for the inverse crack scattering problem”, 日本数学会 2021年度年会, 慶應義塾大学, March 2021.

  8. 古屋貴士, “The monotonicity method for the inverse crack scattering problem”, RIMS共同研究, 京都大学数理解析研究所, January 2021.

2020

  1. 古屋貴士, “亀裂散乱逆問題に対するmonotonicity法について”, 広島数理解析セミナー, November 2020.

  2. 古屋貴士, “散乱逆問題におけるサンプリング法について”, 若手研究者によるポスター展示, October 2020.

  3. 古屋貴士, “Direct and inverse scattering problems for the local perturbation of an open periodic waveguide in the half plane”, 日本数学会 2020年度年会, 日本大学理工学部, March 2020.

  4. 古屋貴士, “Direct and inverse scattering problems for the local perturbation of an open periodic waveguide in the half plane”, RIMS共同研究, 京都大学数理解析研究所, January 2020.

2019

  1. T. Furuya, “The direct and inverse scattering problem for the semilinear Schrödinger equation”, Inverse Problems Reading Group, University of Jyväskylä, Finland, September 2019.

  2. T. Furuya, “The monotonicity based method for the inverse crack scattering problem”, Applied Inverse Problems Conference, Grenoble, France, July 2019.

  3. T. Furuya, “The monotonicity based method for the inverse crack scattering problem”, IPIA Young Scholars Symposium, Beijing, China, June 2019.

  4. T. Furuya, “A modification of the factorization method for scatterers with different physical properties”, Oberseminar Angewandte Analysis und Numerik, Goethe University, Germany, February 2019.

2018

  1. T. Furuya, “A modification of the factorization method for scatterers with different physical properties”, Arbeitsgemeinschaft Inverse Probleme, Karlsruhe Institute of Technology, Germany, October 2018.

  2. T. Furuya, “A modification of the factorization method for scatterers with different physical properties”, International Conference on Inverse Problems and Related Topics, National University of Singapore, August 2018.

  3. 古屋貴士, “The factorization method for some inverse acoustic scattering problem with limited aperture data”, 若手研究集会, 同志社大学, August 2018.

  4. 古屋貴士, “The factorization method for the acoustic inverse scattering problems”, 名古屋微分方程式セミナー, 名古屋大学, April 2018.

  5. 古屋貴士, “An application of the factorization method for some inverse acoustic scattering problems”, 日本数学会 2018年度年会, 東京大学, March 2018.

  6. 古屋貴士, “The factorization method for the acoustic inverse scattering problems”, 第14回数学総合若手研究集会, 北海道大学, February 2018.

  7. 古屋貴士, “An application of the factorization method for some inverse acoustic scattering problems”, 偏微分方程式姫路研究集会, イーグレ姫路, February 2018.

  8. 古屋貴士, “A modification of the factorization method for scatterers with different physical properties”, RIMS共同研究, 京都大学数理解析研究所, January 2018.