论文标题
通过神经波形的傅立叶系数使用非限定输出尺寸设计
Designing with Non-Finite Output Dimension via Fourier Coefficients of Neural Waveforms
论文作者
论文摘要
普通的深度学习模型需要在培训和操作之前由人类从业人员确定其产量的维度。对于设计任务，这对神经网络产生的任何设计的最大复杂性进行了严格的限制，如果更复杂的津贴会导致更好的设计，这将是不利的。在本文中，我们引入了一种方法，用于通过学习“神经波形”，然后作为输出以其傅立叶级数表示的系数来获取非有限维度的输出。然后，我们提供了实验证据，表明神经网络可以在这种情况下学习玩具问题。
Ordinary Deep Learning models require having the dimension of their outputs determined by a human practitioner prior to training and operation. For design tasks, this places a hard limit on the maximum complexity of any designs produced by a neural network, which is disadvantageous if a greater allowance for complexity would result in better designs. In this paper, we introduce a methodology for taking outputs of non-finite dimension from neural networks, by learning a "neural waveform," and then taking as outputs the coefficients of its Fourier series representation. We then present experimental evidence that neural networks can learn in this setting on a toy problem.
