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Takashi Furuya
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Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation
JAL Benitez, T Furuya, F Faucher, X Tricoche, A Kratsios, MV de Hoop
Journal of Computational Physics, 2024, 2024
28*2024
The factorization and monotonicity method for the defect in an open periodic waveguide
T Furuya
Journal of Inverse and Ill-posed Problems 28 (6), 783-796, 2020
112020
Consistency of the Bayes method for the inverse scattering problem
T Furuya, PZ Kow, JN Wang
Inverse Problems 40 (5), 055001, 2024
92024
Globally injective and bijective neural operators
T Furuya, M Puthawala, M Lassas, MV de Hoop
Advances in Neural Information Processing Systems 36, 2024
92024
Scattering by the local perturbation of an open periodic waveguide in the half plane
T Furuya
Journal of Mathematical Analysis and Applications 489 (1), 124149, 2020
92020
The monotonicity method for the inverse crack scattering problem
T Daimon, T Furuya, R Saiin
Inverse Problems in Science and Engineering 28 (11), 1570-1581, 2020
82020
Hermite expansions of some tempered distributions
H Chihara, T Furuya, T Koshikawa
Journal of Pseudo-Differential Operators and Applications 9, 105-124, 2018
62018
Spectral pruning for recurrent neural networks
T Furuya, K Suetake, K Taniguchi, H Kusumoto, R Saiin, T Daimon
International Conference on Artificial Intelligence and Statistics, 3458-3482, 2022
52022
Remarks on the factorization and monotonicity method for inverse acoustic scatterings
T Furuya
Inverse Problems 37 (6), 065006, 2021
52021
Transformers are universal in-context learners
T Furuya, MV de Hoop, G Peyré
arXiv preprint arXiv:2408.01367, 2024
42024
Inverse medium scattering problems with Kalman filter techniques
T Furuya, R Potthast
Inverse Problems 38 (9), 095003, 2022
42022
A modification of the factorization method for scatterers with different physical properties
T Furuya
Mathematical Methods in the Applied Sciences 42 (11), 4017-4030, 2019
42019
The direct and inverse scattering problem for the semilinear Schrödinger equation
T Furuya
Nonlinear Differential Equations and Applications NoDEA 27, 1-17, 2020
32020
Mixture of Experts Soften the Curse of Dimensionality in Operator Learning
A Kratsios, T Furuya, A Lara, M Lassas, M de Hoop
arXiv preprint arXiv:2404.09101, 2024
22024
Consistency of the Bayes method for the inverse scattering problem with Randomly truncated Gaussian priors
T FURUYA, PUZ KOW, JNAN WANG
Manuscript, 2024
12024
Theoretical Error Analysis of Entropy Approximation for Gaussian Mixture
T Furuya, H Kusumoto, K Taniguchi, N Kanno, K Suetake
arXiv e-prints, arXiv: 2202.13059, 2022
1*2022
Simultaneously Solving FBSDEs with Neural Operators of Logarithmic Depth, Constant Width, and Sub-Linear Rank
T Furuya, A Kratsios
arXiv preprint arXiv:2410.14788, 2024
2024
Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations
T Furuya, K Taniguchi, S Okuda
arXiv preprint arXiv:2410.02151, 2024
2024
Approximation Rates and VC-Dimension Bounds for (P) ReLU MLP Mixture of Experts
A Kratsios, H Sáez de Ocáriz Borde, T Furuya, MT Law
arXiv e-prints, arXiv: 2402.03460, 2024
2024
Convergences for Minimax Optimization Problems over Infinite-Dimensional Spaces Towards Stability in Adversarial Training
T Furuya, S Okuda, K Suetake, Y Sawada
Transactions on Machine Learning Research, 2024
2024
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Articles 1–20