| A scalable laplace approximation for neural networks H Ritter, A Botev, D Barber 6th International Conference on Learning Representations, ICLR 2018 …, 2018 | 275 | 2018 |
| Online structured laplace approximations for overcoming catastrophic forgetting H Ritter, A Botev, D Barber Advances in Neural Information Processing Systems 31, 2018 | 225 | 2018 |
| Practical gauss-newton optimisation for deep learning A Botev, H Ritter, D Barber International Conference on Machine Learning, 557-565, 2017 | 176 | 2017 |
| Hamiltonian generative networks P Toth, DJ Rezende, A Jaegle, S Racanière, A Botev, I Higgins arXiv preprint arXiv:1909.13789, 2019 | 161 | 2019 |
| Nesterov's accelerated gradient and momentum as approximations to regularised update descent A Botev, G Lever, D Barber 2017 International joint conference on neural networks (IJCNN), 1899-1903, 2017 | 135 | 2017 |
| Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification A Botev, B Zheng, D Barber AISTATS 54, 1030-1038, 2017 | 28 | 2017 |
| Better, faster fermionic neural networks JS Spencer, D Pfau, A Botev, WMC Foulkes arXiv preprint arXiv:2011.07125, 2020 | 25 | 2020 |
| Disentangling by subspace diffusion D Pfau, I Higgins, A Botev, S Racanière Advances in Neural Information Processing Systems 33, 17403-17415, 2020 | 20 | 2020 |
| Which priors matter? Benchmarking models for learning latent dynamics A Botev, A Jaegle, P Wirnsberger, D Hennes, I Higgins arXiv preprint arXiv:2111.05458, 2021 | 15 | 2021 |
| Deep learning without shortcuts: Shaping the kernel with tailored rectifiers G Zhang, A Botev, J Martens arXiv preprint arXiv:2203.08120, 2022 | 10 | 2022 |
| Sampling QCD field configurations with gauge-equivariant flow models R Abbott, MS Albergo, A Botev, D Boyda, K Cranmer, DC Hackett, ... arXiv preprint arXiv:2208.03832, 2022 | 6 | 2022 |
| Dealing with a large number of classes--Likelihood, Discrimination or Ranking? D Barber, A Botev arXiv preprint arXiv:1606.06959, 2016 | 5 | 2016 |
| Symetric: measuring the quality of learnt hamiltonian dynamics inferred from vision I Higgins, P Wirnsberger, A Jaegle, A Botev Advances in Neural Information Processing Systems 34, 25591-25605, 2021 | 4 | 2021 |
| Aspects of scaling and scalability for flow-based sampling of lattice QCD R Abbott, MS Albergo, A Botev, D Boyda, K Cranmer, DC Hackett, ... arXiv preprint arXiv:2211.07541, 2022 | 3 | 2022 |
| The Gauss-Newton matrix for deep learning models and its applications A Botev UCL (University College London), 2020 | 2 | 2020 |
| Overdispersed variational autoencoders H Shah, D Barber, A Botev 2017 International Joint Conference on Neural Networks (IJCNN), 1109-1116, 2017 | 1 | 2017 |
| Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation B He, J Martens, G Zhang, A Botev, A Brock, SL Smith, YW Teh arXiv preprint arXiv:2302.10322, 2023 | | 2023 |
David BarberDepartment of Computer Science, University College LondonVerified email at ucl.ac.uk
