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Aleksandar Botev
Aleksandar Botev
Google Deepmind
Verified email at google.com
Title
Cited by
Cited by
Year
A scalable laplace approximation for neural networks
H Ritter, A Botev, D Barber
6th International Conference on Learning Representations, ICLR 2018 …, 2018
1982018
Online structured laplace approximations for overcoming catastrophic forgetting
H Ritter, A Botev, D Barber
Advances in Neural Information Processing Systems 31, 2018
1732018
Practical gauss-newton optimisation for deep learning
A Botev, H Ritter, D Barber
International Conference on Machine Learning, 557-565, 2017
1392017
Hamiltonian generative networks
P Toth, DJ Rezende, A Jaegle, S Racanière, A Botev, I Higgins
arXiv preprint arXiv:1909.13789, 2019
1252019
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
1072017
Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification
A Botev, B Zheng, D Barber
AISTATS 54, 1030-1038, 2017
252017
Better, faster fermionic neural networks
JS Spencer, D Pfau, A Botev, WMC Foulkes
arXiv preprint arXiv:2011.07125, 2020
142020
Disentangling by subspace diffusion
D Pfau, I Higgins, A Botev, S Racanière
Advances in Neural Information Processing Systems 33, 17403-17415, 2020
132020
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
72021
Dealing with a large number of classes--Likelihood, Discrimination or Ranking?
D Barber, A Botev
arXiv preprint arXiv:1606.06959, 2016
52016
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers
G Zhang, A Botev, J Martens
arXiv preprint arXiv:2203.08120, 2022
22022
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
12021
Overdispersed variational autoencoders
H Shah, D Barber, A Botev
2017 International Joint Conference on Neural Networks (IJCNN), 1109-1116, 2017
12017
The Gauss-Newton matrix for Deep Learning models and its applications
A Botev
UCL (University College London), 2020
2020
Disentangling by Subspace Diffusion Download PDF
D Pfau, I Higgins, A Botev, S Racaniere
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Articles 1–15