Aleksandar Botev

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 26

Top Topics

Top Venues

Publications

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, Caglar Gulcehre
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models. CoRR (2024)
Aleksandar Botev, Soham De, Samuel L. Smith, Anushan Fernando, George-Cristian Muraru, Ruba Haroun, Leonard Berrada, Razvan Pascanu, Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Sertan Girgin, Olivier Bachem, Alek Andreev, Kathleen Kenealy, Thomas Mesnard, Cassidy Hardin, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Armand Joulin, Noah Fiedel, Evan Senter, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, David Budden, Arnaud Doucet, Sharad Vikram, Adam Paszke, Trevor Gale, Sebastian Borgeaud, Charlie Chen, Andy Brock, Antonia Paterson, Jenny Brennan, Meg Risdal, Raj Gundluru, Nesh Devanathan, Paul Mooney, Nilay Chauhan, Phil Culliton, Luiz GUStavo Martins, Elisa Bandy, David Huntsperger, Glenn Cameron, Arthur Zucker, Tris Warkentin, Ludovic Peran, Minh Giang, Zoubin Ghahramani, Clément Farabet, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Yee Whye Teh, Nando de Frietas
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. CoRR (2024)
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
Applications of flow models to the generation of correlated lattice QCD ensembles. CoRR (2024)
Bobby He, James Martens, Guodong Zhang, Aleksandar Botev, Andrew Brock, Samuel L. Smith, Yee Whye Teh
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation. CoRR (2023)
Bobby He, James Martens, Guodong Zhang, Aleksandar Botev, Andrew Brock, Samuel L. Smith, Yee Whye Teh
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation. ICLR (2023)
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
Normalizing flows for lattice gauge theory in arbitrary space-time dimension. CoRR (2023)
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
Aspects of scaling and scalability for flow-based sampling of lattice QCD. CoRR (2022)
Guodong Zhang, Aleksandar Botev, James Martens
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers. ICLR (2022)
Guodong Zhang, Aleksandar Botev, James Martens
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers. CoRR (2022)
Aleksandar Botev, Andrew Jaegle, Peter Wirnsberger, Daniel Hennes, Irina Higgins
Which priors matter? Benchmarking models for learning latent dynamics. NeurIPS Datasets and Benchmarks (2021)
Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision. CoRR (2021)
Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision. NeurIPS (2021)
Aleksandar Botev, Andrew Jaegle, Peter Wirnsberger, Daniel Hennes, Irina Higgins
Which priors matter? Benchmarking models for learning latent dynamics. CoRR (2021)
David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière
Disentangling by Subspace Diffusion. CoRR (2020)
Peter Toth, Danilo J. Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins
Hamiltonian Generative Networks. ICLR (2020)
James S. Spencer, David Pfau, Aleksandar Botev, W. Matthew C. Foulkes
Better, Faster Fermionic Neural Networks. CoRR (2020)
David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière
Disentangling by Subspace Diffusion. NeurIPS (2020)
Peter Toth, Danilo Jimenez Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins
Hamiltonian Generative Networks. CoRR (2019)
Hippolyt Ritter, Aleksandar Botev, David Barber
Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting. CoRR (2018)
Hippolyt Ritter, Aleksandar Botev, David Barber
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting. NeurIPS (2018)
Hippolyt Ritter, Aleksandar Botev, David Barber
A Scalable Laplace Approximation for Neural Networks. ICLR (Poster) (2018)
Harshil Shah, David Barber, Aleksandar Botev
Overdispersed variational autoencoders. IJCNN (2017)
Aleksandar Botev, Guy Lever, David Barber
Nesterov's accelerated gradient and momentum as approximations to regularised update descent. IJCNN (2017)
Aleksandar Botev, Hippolyt Ritter, David Barber
Practical Gauss-Newton Optimisation for Deep Learning. ICML (2017)
Aleksandar Botev, Bowen Zheng, David Barber
Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification. AISTATS (2017)
Aleksandar Botev, Guy Lever, David Barber
Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent. CoRR (2016)