1. Bentley, P.J., Lim, S.L., Gaier, A., Tran, L.: Coil: Constrained optimization in learned latent space - learning representations for valid solutions. CoRR abs/2202.02163 (2022). https://doi.org/10.48550/arXiv.2202.02163
2. Chang, O., Kwiatkowski, R., Chen, S., Lipson, H.: Agent embeddings: a latent representation for pole-balancing networks. In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2019, pp. 656–664. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2019). https://dl.acm.org/doi/10.5555/3306127.3331753
3. Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, vol. 70, pp. 1126–1135. JMLR.org (2017). https://dl.acm.org/doi/10.5555/3305381.3305498
4. Gaier, A., Asteroth, A., Mouret, J.B.: Discovering representations for black-box optimization. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, GECCO 2020, pp. 103–111. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3377930.3390221
5. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014). https://doi.org/10.1109/CVPR.2014.81