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我们将考虑流行的神经序列处理模型(如RNN和Transformer)与形式化模型(如自动机及其变体)之间的关系。特别地，我们将讨论几种RNN的提取方法，以及通过自动机变体来理解的各种RNN体系结构之间的差异。然后我们将考虑更现代的Transformer。特别是，我们将展示它如何(不!)与现有的正式类相关，并以编程语言的形式提出另一种抽象。
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Formal Abstractions of Neural Sequence Models
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In this work, we propose a new model called triple-path attentive recurrent network (TPARN) for multichannel speech enhancement in the time domain. TPARN extends a single-channel dual-path network to a multichannel network by adding a third path along the spatial dimension. First, TPARN processes speech signals from all channels independently using a dual-path attentive recurrent network (ARN), which is a recurrent neural network (RNN) augmented with self-attention. Next, an ARN is introduced along the spatial dimension for spatial context aggregation. TPARN is designed as a multiple-input and multiple-output architecture to enhance all input channels simultaneously. Experimental results demonstrate the superiority of TPARN over existing state-of-the-art approaches.
