Symbol Emergence and Representation Learning by Integrating GPLVM and Neural Network
Author:
Affiliation:
1. The University of Electro-Communications
2. The University of Tokyo
3. Ritsumeikan University
Publisher
The Robotics Society of Japan
Link
https://www.jstage.jst.go.jp/article/jrsj/42/7/42_42_696/_pdf
Reference10 articles.
1. 1) T. Taniguchi, T. Nagai, T. Nakamura, N. Iwahashi, T. Ogata and H. Asoh: “Symbol emergence in robotics: A survey,” Advanced Robotics, vol.30, issue 11, pp.706–728, 2016.
2. 2) Y. Hagiwara, K. Furukawa, A. Taniguchi and T. Taniguchi: “Multiagent multimodal categorization for symbol emergence: emergent communication via interpersonal cross-modal inference,” Advanced Robotics, vol.36, no.5–6, pp.239–260, 2022.
3. 3) T. Taniguchi, Y. Yoshida, A. Taniguchi and Y. Hagiwara: “Emergent communication through metropolis-hastings naming game with deep generative models,” arXiv preprint arXiv:2205.12392, 2022.
4. 4) T. Nakamura, T. Nagai and T. Taniguchi: “Serket: An architecture for connecting stochastic models to realize a large-scale cognitive model,” arXiv preprint arXiv:1712.00929, 2017.
5. 5) T. Taniguchi, T. Nakamura, M. Suzuki, R. Kuniyasu, K. Hayashi, A. Taniguchi, T. Horii and T. Nagai: “Neuro-serket: development of integrative cognitive system through the composition of deep probabilistic generative models,” New Generation Computing, pp.1–26, 2020.
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