Equivalence relation between the back propagation learning process of an FNN and that of an FNNG. - Qi Jia 006; Katsuyuki Hagiwara; Naohiro Toda; Shiro Usui (Neural Networks, 1994)

Analysis of the error back-propagation learning algorithms with gain. - Qi Jia 006; Katsuyuki Hagiwara; Shiro Usui; Naohiro Toda (Syst. Comput. Jpn., 1995)

An analysis of carp cerebellar eeg under the microgravitational environment. - Shiro Usui; Yutaka Hirata; Katsuyuki Hagiwara; Naohiro Toda; Ichiro Akagi (Syst. Comput. Jpn., 1996)

On the Model Selection for Linear Combination of Step-Type Basis Functions. - Taichi Hayasaka; Katsuyuki Hagiwara; Naohiro Toda; Shiro Usui (ICONIP (1), 1997)

Upper Bounds on the Expected Training Errors of Neural Networks Regressions for a Gaussian Noise. - Katsuyuki Hagiwara; Kazuhiro Kuno; Shiro Usui (ICONIP, 1998)

Regularization Learning and Early Stopping in Linear Networks. - Katsuyuki Hagiwara; Kazuhiro Kuno (IJCNN (4), 2000)

On the Problem in Model Selection of Neural Network Regression in Overrealizable Scenario. - Katsuyuki Hagiwara; Kazuhiro Kuno; Shiro Usui (IJCNN (6), 2000)

Upper bound of the expected training error of neural network regression for a Gaussian noise sequence. - Katsuyuki Hagiwara; Taichi Hayasaka; Naohiro Toda; Shiro Usui; Kazuhiro Kuno (Neural Networks, 2001)

Regularization learning, early stopping and biased estimator. - Katsuyuki Hagiwara (Neurocomputing, 2002)

On the Problem in Model Selection of Neural Network Regression in Overrealizable Scenario. - Katsuyuki Hagiwara (Neural Comput., 2002)

Model Selection with Componentwise Shrinkage in Orthogonal Regression. - Katsuyuki Hagiwara (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2003)

On the Expected Prediction Error of Orthogonal Regression with Variable Components. - Katsuyuki Hagiwara; Hiroshi Ishitani (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2006)

Orthogonal Shrinkage Methods for Nonparametric Regression under Gaussian Noise. - Katsuyuki Hagiwara (ICONIP (1), 2007)

Orthogonalization and Thresholding Method for a Nonparametric Regression Problem. - Katsuyuki Hagiwara (ICONIP (2), 2008)

Relation between weight size and degree of over-fitting in neural network regression. - Katsuyuki Hagiwara; Kenji Fukumizu (Neural Networks, 2008)

On a training scheme based on orthogonalization and thresholding for a nonparametric regression problem. - Katsuyuki Hagiwara (IJCNN, 2010)

Nonparametric Regression Method Based on Orthogonalization and Thresholding. - Katsuyuki Hagiwara (IEICE Trans. Inf. Syst., 2011)

Least Angle Regression in Orthogonal Case. - Katsuyuki Hagiwara (ICONIP (2), 2014)

A Sparse Modeling Method Based on Reduction of Cost Function in Regularized Forward Selection. - Katsuyuki Hagiwara (IEICE Trans. Inf. Syst., 2014)

A Problem in Model Selection of LASSO and Introduction of Scaling. - Katsuyuki Hagiwara (ICONIP (2), 2016)

On scaling of soft-thresholding estimator. - Katsuyuki Hagiwara (Neurocomputing, 2016)

A Scaling and Non-Negative Garrote in Soft-Thresholding. - Katsuyuki Hagiwara (IEICE Trans. Inf. Syst., 2017)

On a Fitting of a Heaviside Function by Deep ReLU Neural Networks. - Katsuyuki Hagiwara (ICONIP (1), 2018)

On an improvement of LASSO by scaling. - Katsuyuki Hagiwara (CoRR, 2018)

A Model Selection Criterion for LASSO Estimate with Scaling. - Katsuyuki Hagiwara (ICONIP (2), 2019)

Bridging between Soft and Hard Thresholding by Scaling. - Katsuyuki Hagiwara (IEICE Trans. Inf. Syst., 2022)

On Gradient Descent Training Under Data Augmentation with On-Line Noisy Copies. - Katsuyuki Hagiwara (IEICE Trans. Inf. Syst., 2023)

Over-parameterized regression methods and their application to semi-supervised learning. - Katsuyuki Hagiwara (CoRR, 2024)

An extension of linear self-attention for in-context learning. - Katsuyuki Hagiwara (CoRR, 2025)