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Conference papers

Sequence Metric Learning as Synchronization of Recurrent Neural Networks

Paul Compagnon 1 Grégoire Lefebvre 2 Stefan Duffner 1 Christophe Garcia 1 
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Sequence metric learning is becoming a widely adopted approach for various applications dealing with sequential multi-variate data such as activity recognition or natural language processing. It is most of the time tackled with sequence alignment approaches or representation learning. In this paper, we propose to study this subject from the point of view of dynamical system theory by drawing the analogy between synchronized trajectories produced by dynamical systems and the distance between similar sequences processed by a siamese recurrent neural network. Indeed, a siamese recurrent network comprises two identical sub-networks, two identical dynamical systems which can theoretically achieve complete synchronization if a coupling is introduced between them. We therefore propose a new neural network model that implements this coupling with a new gate integrated into the classical Gated Recurrent Unit architecture. This model is thus able to simultaneously learn a similarity metric and the synchronization of unaligned multi-variate sequences in a weakly supervised way. Our experiments show that introducing such a coupling improves the performance of the siamese Gated Recurrent Unit architecture on two datasets: one dedicated to activity recognition and another to transportation recognition.
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Submitted on : Friday, June 18, 2021 - 8:59:43 AM
Last modification on : Sunday, June 26, 2022 - 3:09:52 AM
Long-term archiving on: : Sunday, September 19, 2021 - 6:13:26 PM


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  • HAL Id : hal-03264242, version 1


Paul Compagnon, Grégoire Lefebvre, Stefan Duffner, Christophe Garcia. Sequence Metric Learning as Synchronization of Recurrent Neural Networks. International Joint Conference on Neural Networks, Jul 2021, Glasgow (virtuelle ), United Kingdom. ⟨hal-03264242⟩



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