[Submitted on 3 Sep 2026]
Title:Modular Deep Recurrent Neural Network: Application to Quadrotors
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Abstract:A modular deep Recurrent Neural Network (RNN) is introduced to facilitate the process of deploying various architectures of RNNs, and to automatically compute derivatives for gradient-based learning methods. The modularity leads to a set of new architectures, one of which includes feedforward inter-layer connections. By adding feedforward inter-layer connections in a multi-layer RNN, it is observed that the capability of the RNN to learn and model high-order dynamics and nonlinearities is significantly improved. The problem of vanishing/exploding gradient in space for a multilayer RNN is also alleviated using feedforward connections. These results are demonstrated using a quadrotor case study, for which a model of the altitude dynamics is learned with our particular network structure, while existing methods are unable to generalize as quickly or at all.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2609.04339 [cs.LG]
(or arXiv:2609.04339v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.04339
arXiv-issued DOI via DataCite (pending registration)
Journal reference: N. Mohajerin and S. L. Waslander, "Modular deep Recurrent Neural Network: Application to quadrotors," 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, CA, USA, 2014, pp. 1374-1379
Related DOI:
https://doi.org/10.1109/SMC.2014.6974106
DOI(s) linking to related resources
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From: Nima Mohajerin [view email] [v1] Thu, 3 Sep 2026 18:06:46 UTC (1,838 KB)
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