Normalizing flows for atomic solids

1 code implementation • 16 Nov 2021 • Peter Wirnsberger, George Papamakarios, Borja Ibarz, Sébastien Racanière, Andrew J. Ballard, Alexander Pritzel, Charles Blundell

We present a machine-learning approach, based on normalizing flows, for modelling atomic solids.

Highly accurate protein structure prediction with AlphaFold

5 code implementations • Nature 2021 • John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis

Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.

Protein Folding Protein Structure Prediction

Targeted free energy estimation via learned mappings

no code implementations • 12 Feb 2020 • Peter Wirnsberger, Andrew J. Ballard, George Papamakarios, Stuart Abercrombie, Sébastien Racanière, Alexander Pritzel, Danilo Jimenez Rezende, Charles Blundell

Here, we cast Targeted FEP as a machine learning problem in which the mapping is parameterized as a neural network that is optimized so as to increase overlap.

Metacontrol for Adaptive Imagination-Based Optimization

1 code implementation • 7 May 2017 • Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia

The metacontroller component is a model-free reinforcement learning agent, which decides both how many iterations of the optimization procedure to run, as well as which model to consult on each iteration.

Decision Making Reinforcement Learning

Perspective: Energy Landscapes for Machine Learning

no code implementations • 23 Mar 2017 • Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta, Levent Sagun, Jacob D. Stevenson, David J. Wales

Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences.

BIG-bench Machine Learning

Learning to Navigate in Complex Environments

1 code implementation • 11 Nov 2016 • Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andrew J. Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent SIfre, Koray Kavukcuoglu, Dharshan Kumaran, Raia Hadsell

Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents.

Depth Estimation Depth Prediction +5