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几何深度学习是近几年提出的一个新兴概念,在过去的十年间,深度学习为数据科学带来了一场真正的革命,人们已经建立起了各种各样的用于不同类型数据的神经网络架构(例如,卷积神经网络、图神经网络、Transformer、LSTM 等),然而至今仍然缺乏统一的深度神经网络架构设计原理。因此,我们很难理解不同模式之间的关系,也不可避免地导致对相同的概念进行重复发明和重复命名。几何深度学习的提出即是为了对深度学习进行相似的统一。来自DeepMind的AI科学家Michael Bronstein发表了一篇长达160页的论文,试图从对称性和不变性的视角从几何上统一CNNs、GNNs、LSTMs、Transformers等典型架,作者还为此创建了一个网站。希望沐神能讲解一下这篇论文,非常感谢。
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