Aleksandar Botev

According to our database, Aleksandar Botev authored at least 18 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.

Soham De, Samuel L. Smith, Anushan Fernando, Aleksandar Botev, George-Cristian Muraru, Albert Gu, Ruba Haroun, Leonard Berrada, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, Arnaud Doucet, David Budden, Yee Whye Teh, Razvan Pascanu, Nando de Freitas, Çaglar Gülçehre

CoRR, 2024

Applications of flow models to the generation of correlated lattice QCD ensembles.

Ryan Abbott, Aleksandar Botev, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2024

2023

Normalizing flows for lattice gauge theory in arbitrary space-time dimension.

Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2023

Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation.

Bobby He, James Martens, Guodong Zhang, Aleksandar Botev, Andrew Brock, Samuel L. Smith, Yee Whye Teh

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Aspects of scaling and scalability for flow-based sampling of lattice QCD.

Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2022

Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers.

Guodong Zhang, Aleksandar Botev, James Martens

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision.

Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Which priors matter? Benchmarking models for learning latent dynamics.

Aleksandar Botev, Andrew Jaegle, Peter Wirnsberger, Daniel Hennes, Irina Higgins

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020

The Gauss-Newton matrix for Deep Learning models and its applications.

Aleksandar Botev

PhD thesis, 2020

Better, Faster Fermionic Neural Networks.

James S. Spencer, David Pfau, Aleksandar Botev, W. Matthew C. Foulkes

CoRR, 2020

Disentangling by Subspace Diffusion.

David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Hamiltonian Generative Networks.

Peter Toth, Danilo J. Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins

Proceedings of the 8th International Conference on Learning Representations, 2020

2018

Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting.

Hippolyt Ritter, Aleksandar Botev, David Barber

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Scalable Laplace Approximation for Neural Networks.

Hippolyt Ritter, Aleksandar Botev, David Barber

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Overdispersed variational autoencoders.

Harshil Shah, David Barber, Aleksandar Botev

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Nesterov's accelerated gradient and momentum as approximations to regularised update descent.

Aleksandar Botev, Guy Lever, David Barber

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Practical Gauss-Newton Optimisation for Deep Learning.

Aleksandar Botev, Hippolyt Ritter, David Barber

Proceedings of the 34th International Conference on Machine Learning, 2017

Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification.

Aleksandar Botev, Bowen Zheng, David Barber

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017