Long short-term relevance learning

• URL: http://arxiv.org/abs/2106.12694v1
• Date: Mon, 21 Jun 2021 09:07:17 GMT
• Title: Long short-term relevance learning
• Authors: Bram van de Weg, Lars Greve, Bojana Rosic
• Abstract summary: An efficient sparse Bayesian training algorithm is introduced to the network architecture. The proposed scheme automatically determines relevant neural connections and adapts accordingly. We show that the self-regulating framework does not require prior knowledge of a suitable network architecture and size.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: To incorporate prior knowledge as well as measurement uncertainties in the traditional long short term memory (LSTM) neural networks, an efficient sparse Bayesian training algorithm is introduced to the network architecture. The proposed scheme automatically determines relevant neural connections and adapts accordingly, in contrast to the classical LSTM solution. Due to its flexibility, the new LSTM scheme is less prone to overfitting, and hence can approximate time dependent solutions by use of a smaller data set. On a structural nonlinear finite element application we show that the self-regulating framework does not require prior knowledge of a suitable network architecture and size, while ensuring satisfying accuracy at reasonable computational cost.

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