A structure trainable neural network with embedded gating units and its learning algorithm

Abstract

Many problems solved by multilayer neural networks (MLNNs) are reduced into pattern mapping. If the mapping includes several different rules, it is difficult to solve these problems by using a single MLNN with linear connection weights and continuous activation functions.In this paper, a structure trainable neural network has been proposed. The gate units are embedded, which can be trained together with the connection weights. Pattern mapping problems, which include several different mapping rules, can be realized using a single new network. Since, some parts of the network can be commonly used for different mapping rules, the network size can be reduced compared with the modular neural networks, which consists of several independent expert networks.

Large-scale predictive modeling and analytics through regression queries in data management systems

“…To address this, one may think to perform data-space analyses only once, seeking to derive all local linear regression models for the whole of the data space, and use the derived models for all queries. Indeed, a literature survey reveals several methods like [12,42], which identify the nonlinearity of data function g and provide multiple local linear approximations. Unfortunately, these methods are very computationally expensive and thus do not scale with the size n of the data points.…”