论文标题
神经网络适应数据集：学习网络大小和拓扑
Neural networks adapting to datasets: learning network size and topology
论文作者
论文摘要
我们引入了灵活的设置，允许神经网络在基于标准梯度的培训过程中学习其大小和拓扑。最终的网络具有针对特定学习任务和数据集的图表的结构。所获得的网络也可以从头开始训练，并实现几乎相同的性能。我们探讨了许多不同难以观察系统规律性的难度数据集的网络体系结构的属性。因此，获得的图可以理解为编码特定分类任务的非平凡特征。
We introduce a flexible setup allowing for a neural network to learn both its size and topology during the course of a standard gradient-based training. The resulting network has the structure of a graph tailored to the particular learning task and dataset. The obtained networks can also be trained from scratch and achieve virtually identical performance. We explore the properties of the network architectures for a number of datasets of varying difficulty observing systematic regularities. The obtained graphs can be therefore understood as encoding nontrivial characteristics of the particular classification tasks.
