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Neural Cellular Automata (NCAs) have been proven effective in simulating morphogenetic processes, the continuous construction of complex structures from very few starting cells.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014) · 1958
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Theory of self-reproducing automata
Neumann, J., Burks, A. W., et al. (1966) · 1966
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The fantastic combinations of john conway’s new solitaire game “life” by martin gardner
Games, M. (1970) · 1970
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Simulating physics with cellular automata
Vichniac, G. Y. (1984) · 1984
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Cellular automata as models of complexity
Wolfram, S. (1984) · 1984
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Studying artificial life with cellular automata
Langton, C. G. (1986) · 1986
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Neural-network-based cellular automata for simulating multiple land use changes using gis
Li, X. and Yeh, A. G.-O. (2002) · 2002
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Materials as morphogenetic guides in tissue engineering
Hubbell, J. A. (2003) · 2003
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The application of bone morphogenetic proteins to dental tissue engineering
Nakashima, M. and Reddi, A. H. (2003) · 2003
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Evocraft: A new challenge for open-endedness
Grbic, D., Palm, R. B., Najarro, E., Glanois, C., and Risi, S. (2020) · 2012
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Epithelial machines of morphogenesis and their potential application in organ assembly and tissue engineering
Joshi, S. D. and Davidson, L. A. (2012) · 2012
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013) · 2013
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Engineering three-dimensional stem cell morphogenesis for the development of tissue models and scalable regenerative therapeutics
Kinney, M. A., Hookway, T. A., Wang, Y., and McDevitt, T. C. (2014) · 2014
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Vecerik, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Rothörl, T., Lampe, T., and Riedmiller, M. (2017) · 2017
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019) · 2019
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Growing neural cellular automata
Mordvintsev, A., Randazzo, E., Niklasson, E., and Levin, M. (2020) · 2020
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Zero: Memory optimizations toward training trillion parameter models
Rajbhandari, S., Rasley, J., Ruwase, O., and He, Y. (2020) · 2020
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Regenerating soft robots through neural cellular automata
Horibe, K., Walker, K., and Risi, S. (2021) · 2021
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Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. (2015) · 2015
Cited alongside, same era.
Ca-neat: evolved compositional pattern producing networks for cellular automata morphogenesis and replication
Nichele, S., Ose, M. B., Risi, S., and Tufte, G. (2017) · 2017
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Neural cellular automata manifold
Ruiz, A. H., Vilalta, A., and Moreno-Noguer, F. (2021) · 2021
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Learning to generate 3d shapes with generative cellular automata
Zhang, D., Choi, C., Kim, J., and Kim, Y. (2021) · 2021
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