| Semi-supervised generative modeling for controllable speech synthesis R Habib, S Mariooryad, M Shannon, E Battenberg, RJ Skerry-Ryan, ... arXiv preprint arXiv:1910.01709, 2019 | 66 | 2019 |
| Variational f-divergence minimization M Zhang, T Bird, R Habib, T Xu, D Barber arXiv preprint arXiv:1907.11891, 2019 | 40 | 2019 |
| Spread divergence M Zhang, P Hayes, T Bird, R Habib, D Barber International Conference on Machine Learning, 11106-11116, 2020 | 37 | 2020 |
| Auxiliary variational MCMC R Habib, D Barber 7th International Conference on Learning Representations, ICLR 2019, 1-13, 2019 | 31 | 2019 |
| Sample efficient model evaluation E Yilmaz, P Hayes, R Habib, J Burgess, D Barber arXiv preprint arXiv:2109.12043, 2021 | 8 | 2021 |
| Spread divergences D Barber, M Zhang, R Habib, T Bird | 3 | 2018 |
| Integrated weak learning P Hayes, M Zhang, R Habib, J Burgess, E Yilmaz, D Barber arXiv preprint arXiv:2206.09496, 2022 | 2 | 2022 |
| Training generative latent models by variational f-divergence minimization M Zhang, T Bird, R Habib, T Xu, D Barber | 1 | 2019 |
| Advances in probabilistic deep learning R Habib UCL (University College London), 2022 | | 2022 |
David BarberDepartment of Computer Science, University College LondonVerified email at ucl.ac.uk
