| A scalable laplace approximation for neural networks H Ritter, A Botev, D Barber 6th international conference on learning representations, ICLR 2018 …, 2018 | 513 | 2018 |
| Online structured laplace approximations for overcoming catastrophic forgetting H Ritter, A Botev, D Barber Advances in Neural Information Processing Systems 31, 2018 | 383 | 2018 |
| Practical Gauss-Newton optimisation for deep learning A Botev, H Ritter, D Barber International Conference on Machine Learning, 557-565, 2017 | 285 | 2017 |
| Hamiltonian generative networks P Toth, DJ Rezende, A Jaegle, S Racanière, A Botev, I Higgins arXiv preprint arXiv:1909.13789, 2019 | 260 | 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 | 181 | 2017 |
| Griffin: Mixing gated linear recurrences with local attention for efficient language models S De, SL Smith, A Fernando, A Botev, G Cristian-Muraru, A Gu, R Haroun, ... arXiv preprint arXiv:2402.19427, 2024 | 105 | 2024 |
| Better, faster fermionic neural networks JS Spencer, D Pfau, A Botev, WMC Foulkes arXiv preprint arXiv:2011.07125, 2020 | 57 | 2020 |
| Disentangling by subspace diffusion D Pfau, I Higgins, A Botev, S Racanière Advances in Neural Information Processing Systems 33, 17403-17415, 2020 | 39 | 2020 |
| Nesterov's accelerated gradient and momentum as approximations to regularised update descent A Botev, G Lever, D Barber arXiv preprint arXiv:1607.01981, 2016 | 36 | 2016 |
| 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 | 35 | 2023 |
| Deep learning without shortcuts: Shaping the kernel with tailored rectifiers G Zhang, A Botev, J Martens arXiv preprint arXiv:2203.08120, 2022 | 35 | 2022 |
| Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification A Botev, B Zheng, D Barber AISTATS 54, 1030-1038, 2017 | 34 | 2017 |
| Aspects of scaling and scalability for flow-based sampling of lattice QCD R Abbott, MS Albergo, A Botev, D Boyda, K Cranmer, DC Hackett, ... The European Physical Journal A 59 (11), 257, 2023 | 31 | 2023 |
| 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 | 31 | 2021 |
| 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 | 22 | 2022 |
| Normalizing flows for lattice gauge theory in arbitrary space-time dimension R Abbott, MS Albergo, A Botev, D Boyda, K Cranmer, DC Hackett, ... arXiv preprint arXiv:2305.02402, 2023 | 21 | 2023 |
| Applications of flow models to the generation of correlated lattice QCD ensembles R Abbott, A Botev, D Boyda, DC Hackett, G Kanwar, S Racanière, ... Physical Review D 109 (9), 094514, 2024 | 11 | 2024 |
| Recurrentgemma: Moving past transformers for efficient open language models A Botev, S De, SL Smith, A Fernando, GC Muraru, R Haroun, L Berrada, ... arXiv preprint arXiv:2404.07839, 2024 | 11 | 2024 |
| The Gauss-Newton matrix for deep learning models and its applications A Botev UCL (University College London), 2020 | 11 | 2020 |
| 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 | 6 | 2021 |
David BarberDepartment of Computer Science, University College London在 ucl.ac.uk 的电子邮件经过验证
