Use of neural networks in the problems of operation and repair of rolling stock of electric transport

Abstract

Deep neural networks have achieved great success in controlled learning problems, and MIL as a typical poorly controlled learning method is effective for many applications in computer vision, biometrics, natural language processing, etc. In this article, we review several neural networks with multiple instances ( MINN), which neural networks seek to solve MIL problems. MINNs perform MILs in the end, which take bags with different numbers of instances as input and directly output the tags of the bags. All parameters in MINN can be optimized by back propagation. In addition to revising old MINNs, we offer a new type of MINN for exploring bag representations, which differs from existing MINNs that focus on the evaluation of an instance label. In addition, recent tricks developed in deep learning have been studied in MINN; we find that deep supervision is effective for a better understanding of bag views. In experiments, the proposed MINNs achieve the most advanced or competitive performance on several MIL tests. Moreover, for testing and learning it is very fast, for example, it takes only 0.0.0 03 s to predict the bag and a few seconds to learn on the MIL datasets on a moderate processor. Initially, several instances (MILs) were proposed to predict bounce activity [1]. Now it is widely applied to many domains and is an important problem in computer training. Many multimedia data have a multiplier (MI) structure; For example, a text article contains several paragraphs, the image can be divided into several local areas, and gene expression data contains several genes. MIL is useful for processing and understanding MI data. Studying multiple instances is a type of weak controlled learning (WSL). Each sample is executed in the form of labeled data, which consist of a wide variety of instances associated with the functions of input. The purpose of MIL in the dual task is to prepare a classifier for prediction of test packet labels, based on the assumption that there is a positive packet and contains at least one positive instance, while the data is negative if it consists only of negative instances.