深度学习只能是神经网络吗?

Zhi-Hua Zhou
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引用次数: 0

摘要

“深度学习”一词通常被认为是“深度神经网络”(deep neural networks, dnn)的同义词。在这次演讲中,我们将讨论深度学习的要点,并声称深度学习不一定要通过神经网络和可微模块来实现。然后,我们将对非nn风格的深度学习进行探索,其中构建块是不可微的模块,并且训练过程不依赖于反向传播或基于梯度的调整。我们还将讨论这一研究方向的一些最新进展和挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can Deep Learning Only Be Neural Networks?
The word "deep learning" is generally regarded as a synonym of "deep neural networks (DNNs)". In this talk, we will discuss on essentials in deep learning and claim that deep learning is not necessarily to be realized by neural networks and differentiable modules. We will then present an exploration to non-NN style deep learning, where the building blocks are non-differentiable modules and the training process does not rely on backpropagation or gradient-based adjustment. We will also talk about some recent advances and challenges in this direction of research.
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