论文标题
通过$ L_1 $正则化的3D椭圆形径向基函数神经网络的分子稀疏表示
Molecular Sparse Representation by 3D Ellipsoid Radial Basis Function Neural Networks via $L_1$ Regularization
论文作者
论文摘要
在本文中，我们开发了一个椭圆形径向基函数神经网络（ERBFNN）和算法，用于稀疏代表分子形状。为了评估分子形状模型的稀疏表示，ERBFNN近似具有相对较少数量的神经元的Gussian密度图。深度学习模型是通过使用$ L_1 $正规化优化非线性损失功能来培训的。实验结果表明，原始分子形状能够通过我们的算法以较少的ERBFNN来良好地表示。我们的网络原则上可以应用于分子形和粗粒分子建模的多分辨率稀疏表示。
In this paper, we have developed an ellipsoid radial basis function neural network (ERBFNN) and algorithm for sparse representing of a molecular shape. To evaluate a sparse representation of the molecular shape model, the Gaussian density map of molecule is approximated by ERBFNN with a relatively small number of neurons. The deep learning models were trained by optimizing a nonlinear loss function with $L_1$ regularization. Experimental results demonstrate that the original molecular shape is able to be represented with good accuracy by much fewer scale of ERBFNN by our algorithm. And our network in principle can be applied to multi-resolution sparse representation of molecular shape and coarse-grained molecular modeling.
