… polynomial functions can be strong learning models. Particularly this work constructs
polynomial feedforward neural networks … The new neural network is a polynomial function and …

… We use it in a feedforward network to construct a new type of polynomial neural network, …
in a feedforward neural network, we obtain the Ladder Polynomial Neural Network (LPNN). …

… a deep ladder neural network for SSNC. Our trained ladder neural networks could combine
supervised learning with unsupervised learning in deep neural networks via simultaneously …

… In this work, by viewing an L-layer NN as a ladder of RKHSs with L-levels, we propose a …
In Section 3, we introduce the neural Hilbert ladder (NHL) model and the NHL spaces and …

… dramatically extend the scale of the neural networks with improved performance. We have
… , which assumes that the order of the polynomials of the Laplacian is restricted to 1 while the …

… developed neural-network quantum states in the two-leg Bose-Hubbard ladder under …
machine and feedforward neural network, we show that variational neural networks can reliably …

… where P 2 is the second Legendre polynomial and u is the unit vector along the molecular …
Compared to the τ 2 of 3.3 ps for light water predicted by the SCAN neural network (87) at 330 …

… Alternatively, neural networks (NNs) are envisioned as … NN structures, referred to as the
ladder-wise grid structure (LWGS) … In this article, the polynomial model is used to approximate …

… : a general orthogonal Laguerre network and a Ladder network. The state of such …
polynomials can be used as an orthonormal basis and propose two variants, Laguerre and Ladder …

… To characterize the functions spaces explored by multi-layer neural networks (NNs), we
introduce Neural Hilbert Ladders (NHLs), a collection of reproducing kernel Hilbert spaces (…