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Deep Learning Architectures for Novel Problems

机译：面向新问题的深度学习架构

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摘要

With convolutional neural networks revolutionizing the computer vision field it is important to extend the capabilities of neural-based systems to dynamic and unrestricted data like graphs. Doing so not only expands the applications of such systems, but also provide more insight into improvements to neural-based systems.;Currently most implementations of graph neural networks are based on vertex filtering on fixed adjacency matrices. Although important for a lot of applications, vertex filtering restricts the applications to vertex focused graphs and cannot be effi- ciently extended to edge focused graphs like social networks. Applications of current systems are mostly limited to images and document references.;Beyond the graph applications, this work also explored the usage of convolutional neural networks for intelligent character recognition in a novel way. Most systems define Intelligent Character Recognition as either a recurrent classification problem or image classification. This achieves great performance in a limited environment but does not generalize well on real world applications. This work defines Intelligent Character Recognition as a segmentation problem which we show to provide many benefits.;The goal of this work was to explore alternatives to current graph neural networks implementations as well as exploring new applications of such system. This work also focused on improving Intelligent Character Recognition techniques on isolated words using deep learning techniques. Due to the contrast between these to contributions this documents was divided into Part I focusing on the graph work, and Part II focusing on the intelligent character recognition work.
机译：随着卷积神经网络彻底改变了计算机视觉领域，将基于神经系统的功能扩展到动态且不受限制的数据（如图形）非常重要。这样做不仅扩展了此类系统的应用范围，而且还为基于神经系统的改进提供了更多见识。；目前，大多数图神经网络的实现都是基于固定邻接矩阵上的顶点过滤。尽管对于许多应用程序来说很重要，但是顶点过滤将应用程序限制在以顶点为中心的图上，并且无法有效地扩展到诸如社交网络之类的以边缘为中心的图上。当前系统的应用主要限于图像和文档参考。除了图形应用之外，这项工作还探索了卷积神经网络以一种新颖的方式用于智能字符识别的用途。大多数系统将智能字符识别定义为循环分类问题或图像分类。这在有限的环境中可实现出色的性能，但在现实世界中的应用中无法很好地推广。这项工作将智能字符识别定义为一个分割问题，我们将证明它可以提供很多好处。;这项工作的目的是探索当前图神经网络实现的替代方法以及该系统的新应用。这项工作还专注于使用深度学习技术改善孤立单词的智能字符识别技术。由于这些与贡献之间的对比，本文档分为第一部分专注于图形工作，第二部分专注于智能字符识别工作。

著录项

  • 作者

    Petroski Such, Felipe.;

  • 作者单位

    Rochester Institute of Technology.;

  • 授予单位 Rochester Institute of Technology.;
  • 学科 Computer engineering.;Computer science.;Artificial intelligence.
  • 学位 M.S.
  • 年度 2017
  • 页码 65 p.
  • 总页数 65
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 公共建筑;
  • 关键词

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