The differential quadrature method (DQM) is one of the most elegant and efficient methods for the numerical solution of partial differential equations arising in engineering and applied sciences. It ...

Abstract: This paper proposes a precise signal recovery method utilizing a composition of multiple non-convex regularization functions, termed multilayered non-convex regularization functions, to ...

The recent use of spatial coordinate features in multilayer perceptron (MLP) neural networks provides opportunities for novel applications in potential field geophysics. So-called coordinate MLP ...

17. This exercise is concerned with recovering a function $u(t)$ on the interval $[0,1]$ given noisy data $b_i$ at points $t_i=i h, i=0,1, \ldots, N$, with $N=1 / h ...

When attempting the question, there is a bonus part to add l2 regularization to the softmax regression code (In [75]): According to the book, in the section about Ridge Regression, we are supposed to ...

Abstract: This article investigates the use of extended Kalman filtering to train recurrent neural networks with rather general convex loss functions and regularization terms on the network parameters ...

In simple terms, we can define regularization as a process that simplifies our result answer, often used to prevent overfitting. On the Machine Learning and Deep Learning side, it also helps to ...

Individual wants (preferences) and abilities may partly determine prices or tax rates. The resulting simultaneous changes in prices (or taxes) and preferences make it difficult to estimate policy ...

Reducing the number of features in absence of sufficient training data might be a good option but at same time will result in loss of information that could have been important to the model. Of course ...

A large class of inverse problems arise from a discretized Fredholm integral equation (FIE) of the first kind. With the introduction of upper and lower bounds based on prior knowledge, this may be ...