Search Results for author: Aleksandar Botev

Found 12 papers, 7 papers with code

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

no code implementations14 Nov 2022 Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing.

Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers

1 code implementation ICLR 2022 Guodong Zhang, Aleksandar Botev, James Martens

However, this method (called Deep Kernel Shaping) isn't fully compatible with ReLUs, and produces networks that overfit significantly more than ResNets on ImageNet.

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

1 code implementation NeurIPS 2021 Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev

Using SyMetric, we identify a set of architectural choices that significantly improve the performance of a previously proposed model for inferring latent dynamics from pixels, the Hamiltonian Generative Network (HGN).

Autonomous Driving Image Reconstruction

Which priors matter? Benchmarking models for learning latent dynamics

1 code implementation9 Nov 2021 Aleksandar Botev, Andrew Jaegle, Peter Wirnsberger, Daniel Hennes, Irina Higgins

Learning dynamics is at the heart of many important applications of machine learning (ML), such as robotics and autonomous driving.

Autonomous Driving

Better, Faster Fermionic Neural Networks

1 code implementation13 Nov 2020 James S. Spencer, David Pfau, Aleksandar Botev, W. M. C. Foulkes

The Fermionic Neural Network (FermiNet) is a recently-developed neural network architecture that can be used as a wavefunction Ansatz for many-electron systems, and has already demonstrated high accuracy on small systems.

Disentangling by Subspace Diffusion

1 code implementation NeurIPS 2020 David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière

We present a novel nonparametric algorithm for symmetry-based disentangling of data manifolds, the Geometric Manifold Component Estimator (GEOMANCER).

Metric Learning Representation Learning

Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting

no code implementations NeurIPS 2018 Hippolyt Ritter, Aleksandar Botev, David Barber

In order to make our method scalable, we leverage recent block-diagonal Kronecker factored approximations to the curvature.


A Scalable Laplace Approximation for Neural Networks

1 code implementation ICLR 2018 Hippolyt Ritter, Aleksandar Botev, David Barber

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace and more

Bayesian Inference

Practical Gauss-Newton Optimisation for Deep Learning

no code implementations ICML 2017 Aleksandar Botev, Hippolyt Ritter, David Barber

We present an efficient block-diagonal ap- proximation to the Gauss-Newton matrix for feedforward neural networks.

Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent

no code implementations7 Jul 2016 Aleksandar Botev, Guy Lever, David Barber

We present a unifying framework for adapting the update direction in gradient-based iterative optimization methods.

Dealing with a large number of classes -- Likelihood, Discrimination or Ranking?

no code implementations22 Jun 2016 David Barber, Aleksandar Botev

We consider training probabilistic classifiers in the case of a large number of classes.

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