Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification. - Aleksandar Botev; Bowen Zheng 004; David Barber (AISTATS, 2017)

Practical Gauss-Newton Optimisation for Deep Learning. - Aleksandar Botev; Hippolyt Ritter; David Barber (ICML, 2017)

Nesterov's accelerated gradient and momentum as approximations to regularised update descent. - Aleksandar Botev; Guy Lever; David Barber (IJCNN, 2017)

Overdispersed variational autoencoders. - Harshil Shah; David Barber; Aleksandar Botev (IJCNN, 2017)

A Scalable Laplace Approximation for Neural Networks. - Hippolyt Ritter; Aleksandar Botev; David Barber (ICLR (Poster), 2018)

Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting. - Hippolyt Ritter; Aleksandar Botev; David Barber (NeurIPS, 2018)

Hamiltonian Generative Networks. - Peter Toth; Danilo J. Rezende; Andrew Jaegle; Sébastien Racanière; Aleksandar Botev; Irina Higgins (ICLR, 2020)

Disentangling by Subspace Diffusion. - David Pfau; Irina Higgins; Aleksandar Botev; Sébastien Racanière (NeurIPS, 2020)

Better, Faster Fermionic Neural Networks. - James S. Spencer; David Pfau; Aleksandar Botev; W. Matthew C. Foulkes (CoRR, 2020)

The Gauss-Newton matrix for Deep Learning models and its applications. - Aleksandar Botev (2020)

Which priors matter? Benchmarking models for learning latent dynamics. - Aleksandar Botev; Andrew Jaegle; Peter Wirnsberger; Daniel Hennes; Irina Higgins (NeurIPS Datasets and Benchmarks, 2021)

SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision. - Irina Higgins; Peter Wirnsberger; Andrew Jaegle; Aleksandar Botev (NeurIPS, 2021)

Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers. - Guodong Zhang; Aleksandar Botev; James Martens (ICLR, 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; et al. (CoRR, 2022)

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 (ICLR, 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; et al. (CoRR, 2023)

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)

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 001; Srivatsan Srinivasan; Guillaume Desjardins; Arnaud Doucet; et al. (CoRR, 2024)

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. - 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; et al. (CoRR, 2024)