Abstract
Inspired by the neurons’ differences in membrane time-scales, the multiple timescale recurrent neural network model (MTRNN) adopts the hierarchical architecture with increasing time-scales from bottom to top layers. Based on this idea, the recent adaptive and continuous time recurrent neural networks (ACTRNN) and the gated adaptive continuous time recurrent neural network (GACTRNN) develop the novel learning mechanism on the time-scales. In this paper, we test the performance of GACTRNN using the dataset obtained from a real-world humanoid robot’s object manipulation experiment. By using trainable timescale parameters with the gating mechanism, it can be observed that the GACTRNN can better learn the temporal characteristics of the sequences. Besides, to eliminate the effects of parameters’ overgrowing with a large data-set, we improve the GACTRNN model and propose the MATRNN model. In this model, the sigmoid function is used instead of exponential function. We compare the performances of the CTRNN, GACTRNN and MATRNN models, and find that the GACTRNN and MATRNN models perform better than the CTRNN model with the large-scale dataset. By visualizing the timescales adapting in the training process, we also qualitatively show that the MATRNN model performs better than the GACTRNN model in terms of stability with the dataset.
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This work is partially supported by PolyU Start-up Grant (ZVUY-P0035417).
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Zhao, L., Zhong, J. (2021). Recurrent Neural Network with Adaptive Gating Timescales Mechanisms for Language and Action Learning. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_47
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DOI: https://doi.org/10.1007/978-3-030-92310-5_47
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