Publications

Le, T. et al. (2019) “Revisiting reweighted wake-sleep for models with stochastic control flow”, in Proceedings of the International Conference on Uncertainty in Artificial Intelligence. Association for Uncertainty in Artificial Intelligence.

Byrne, H. et al. (2019) “Topological Methods for Characterising Spatial Networks: A Case Study in Tumour Vasculature”, Mathematics Today [Preprint].

Klimm, F. et al. (2019) “Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks.”

Eric, N. et al. (2019) “Hybrid models with deep and invertible features”, in Proceedings of Machine Learning Research. Proceedings of Machine Learning Research, pp. 4723–4732.

Lee, J. et al. (2019) “Set transformer: A framework for attention-based permutation-invariant neural networks”, in Proceedings of Machine Learning Research. PMLR, pp. 3744–3753.

Goliński, A., Wood, F. and Rainforth, T. (2019) “Amortized Monte Carlo integration”, in Proceedings of Machine Learning Research. Proceedings of Machine Learning Research, pp. 2309–2318.

Lyddon, S., Holmes, C. and Walker, S. (2019) “General Bayesian updating and the loss-likelihood bootstrap”, Biometrika, 106(2), pp. 465–478.

Gram-Hansen, B. et al. (2019) “Hijacking Malaria Simulators with Probabilistic Programming”, arXiv.

Lee, J., Miscouridou, X. and Caron, F. (2019) “A unified construction for series representations and finite approximations of completely random measures”, arXiv.

Lee, J. et al. (2019) “Set transformer: A framework for attention-based permutation-invariant neural networks”, in. Proceedings of Machine Learning Research, pp. 3744–3753.