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Publications

Teh, Y., Dupont, E. and Doucet, A. (2019) “Augmented neural ODEs”, Proceedings of the 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS Edition (EMC2-NIPS 2019), 32(2019), pp. 1–11.
Kosiorek, A. et al. (2019) “Stacked capsule autoencoders”, in Advances in Neural Information Processing Systems 32 (NIPS 2019). Nueral Information Processing Systems, p. 15512.
Foster, A. et al. (2019) “Variational Bayesian optimal experimental design”, in Advances in Neural Information Processing Systems 32 (NIPS 2019). Conference on Neural Information Processing Systems.
Ge, S. et al. (2019) “Random tessellation forests”, in Advances in Neural Information Processing Systems 32: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Curran Associates, pp. 9543–9553.
Rao, D. et al. (2019) “Continual unsupervised representation learning”, in Advances in Neural Information Processing Systems 32 (NIPS 2019). Conference on Neural Information Processing Systems.
Mathieu, E. et al. (2019) “Continuous hierarchical representations with poincaré Variational Auto-Encoder”, in Advances in Neural Information Processing Systems 32: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Curran Associates, pp. 12521–12532.
Foster, A. et al. (2019) “Variational Bayesian Optimal Experimental Design”, in.
Enticott, G. et al. (2019) “An assessment of risk compensation and spillover behavioural adaptions associated with the use of vaccines in animal disease management”, Vaccine, 38(5), pp. 1065–1075.
Parag, K. and Donnelly, C. (2019) “Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation”, p. 835181.
Martínez-Rubio, D., Kanade, V. and Rebeschini, P. (2019) “Decentralized cooperative stochastic bandits”, in Advances in Neural Information Processing Systems 32. Neural Information Processing Systems Foundation.