Multi-layer perceptron (MLP) is a fundamental component of deep learning...
Hopfield networks and Boltzmann machines (BMs) are fundamental energy-ba...
Gradient regularization (GR) is a method that penalizes the gradient nor...
A biological neural network in the cortex forms a neural field. Neurons ...
Sequential training from task to task is becoming one of the major objec...
Data augmentation is widely used for machine learning; however, an effec...
Natural Gradient Descent (NGD) helps to accelerate the convergence of
gr...
The Fisher information matrix (FIM) is fundamental for understanding the...
The Fisher information matrix (FIM) plays an essential role in statistic...
Normalization methods play an important role in enhancing the performanc...
A deep neural network is a hierarchical nonlinear model transforming inp...
Statistical neurodynamics studies macroscopic behaviors of randomly conn...
This study analyzes the Fisher information matrix (FIM) by applying
mean...
Deep generative models are reported to be useful in broad applications
i...