论文标题
图形神经网络用于图像分类和使用图表的增强学习
Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations
论文作者
论文摘要
在本文中，我们将评估两个不同领域中图神经网络的性能：计算机视觉和增强学习。在“计算机视觉部分”中，我们试图了解图像的新型非冗余表示形式是否可以改善琐碎像素的性能，以在图级预测图上，特别是图像分类上的节点映射。对于强化学习部分，我们试图学习是否明确建模解决魔方作为图形问题可以提高性能，而不是无电感偏差的标准无模型技术。
In this paper, we will evaluate the performance of graph neural networks in two distinct domains: computer vision and reinforcement learning. In the computer vision section, we seek to learn whether a novel non-redundant representation for images as graphs can improve performance over trivial pixel to node mapping on a graph-level prediction graph, specifically image classification. For the reinforcement learning section, we seek to learn if explicitly modeling solving a Rubik's cube as a graph problem can improve performance over a standard model-free technique with no inductive bias.
