Related papers: Internal representation dynamics and geometry in recurrent neural networks

Internal representation dynamics and geometry in recurrent neural networks

URL: http://arxiv.org/abs/2001.03255v2
Date: Tue, 14 Jan 2020 14:23:02 GMT
Title: Internal representation dynamics and geometry in recurrent neural networks
Authors: Stefan Horoi, Guillaume Lajoie and Guy Wolf
Abstract summary: We show how a vanilla RNN implements a simple classification task by analysing the dynamics of the network. We find that early internal representations are evocative of the real labels of the data but this information is not directly accessible to the output layer.
Score: 10.016265742591674
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The efficiency of recurrent neural networks (RNNs) in dealing with sequential data has long been established. However, unlike deep, and convolution networks where we can attribute the recognition of a certain feature to every layer, it is unclear what "sub-task" a single recurrent step or layer accomplishes. Our work seeks to shed light onto how a vanilla RNN implements a simple classification task by analysing the dynamics of the network and the geometric properties of its hidden states. We find that early internal representations are evocative of the real labels of the data but this information is not directly accessible to the output layer. Furthermore the network's dynamics and the sequence length are both critical to correct classifications even when there is no additional task relevant information provided.

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