603556-Tonnaer

136 BIBLIOGRAPHY Pérez Rey, L., Tonnaer, L., Menkovski, V., Holenderski, M., and Portegies, J. (2020). A metric for linear symmetry-based disentanglement. InNeurIPS 2020 workshop on Differential Geometry meets Deep Learning (DiffGeo4DL). Pérez Rey, L. A., Menkovski, V., and Portegies, J. W. (2019). Diffusion Variational Autoencoders. IJCAI International Joint Conference on Artificial Intelligence, pages 2704–2710. (Cited on pages 69, 81, 98, 113, and 115.) Pfau, D., Higgins, I., Botev, A., and Racanière, S. (2020). Disentangling by Subspace Diffusion. Advances in Neural Information Processing Systems, 2020Decem. (Cited on page 68.) Pimentel, M. A., Clifton, D. A., Clifton, L., and Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99:215–249. (Cited on pages 6, 26, and27.) Quessard, R., Barrett, T. D., and Clements, W. R. (2020). Learning Group Structure and Disentangled Representations of Dynamical Environments. Advances in Neural Information Processing Systems. (Cited on pages xvi, 7, 52, 58, 59, 67, 76, 82, 83, and 109.) Rezende, D. J., Mohamed, S., and Wierstra, D. (2014). Stochastic Backpropagation and Approximate Inference in Deep Generative Models. 31st International Conference on Machine Learning, ICML 2014, 4:3057–3070. (Cited on pages 5, 11, 27, 28, 29, 68, 76, 108, and 123.) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). "Why Should I Trust You?": Explaining the Predictions of Any Classifier. NAACL-HLT 2016 - 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Demonstrations Session, pages 97–101. (Cited on page 3.) Ridgeway, K. and Mozer, M. C. (2018). Learning Deep Disentangled Embeddings with the F-Statistic Loss. Advances in Neural Information Processing Systems, 2018-December:185–194. (Cited on pages 16 and 77.) Roth, H. R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., and Summers, R. M. (2016). Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation. IEEE transactions on medical imaging, 35(5):1170–1181. (Cited on page 27.)

RkJQdWJsaXNoZXIy MjY0ODMw