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Jonathan Godwin
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- affiliation: Orbital Materials, London, UK
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2020 – today
- 2024
- [i11]Mark Neumann, James Gin, Benjamin Rhodes, Steven Bennett, Zhiyi Li, Hitarth Choubisa, Arthur Hussey, Jonathan Godwin:
Orb: A Fast, Scalable Neural Network Potential. CoRR abs/2410.22570 (2024) - 2023
- [c4]Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin:
Pre-training via Denoising for Molecular Property Prediction. ICLR 2023 - [i10]Tim Bechtel, Daniel T. Speckhard, Jonathan Godwin, Claudia Draxl:
Band-gap regression with architecture-optimized message-passing neural networks. CoRR abs/2309.06348 (2023) - 2022
- [c3]Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia:
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond. ICLR 2022 - [c2]Kimberly L. Stachenfeld, Drummond Buschman Fielding, Dmitrii Kochkov, Miles D. Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter W. Battaglia, Alvaro Sanchez-Gonzalez:
Learned Simulators for Turbulence. ICLR 2022 - [i9]Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin:
Pre-training via Denoising for Molecular Property Prediction. CoRR abs/2206.00133 (2022) - [i8]Michael Schaarschmidt, Morgane Riviere, Alex M. Ganose, James S. Spencer, Alexander L. Gaunt, James Kirkpatrick, Simon Axelrod, Peter W. Battaglia, Jonathan Godwin:
Learned Force Fields Are Ready For Ground State Catalyst Discovery. CoRR abs/2209.12466 (2022) - 2021
- [i7]Kimberly L. Stachenfeld, Jonathan Godwin, Peter W. Battaglia:
Graph Networks with Spectral Message Passing. CoRR abs/2101.00079 (2021) - [i6]Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia:
Very Deep Graph Neural Networks Via Noise Regularisation. CoRR abs/2106.07971 (2021) - [i5]Ravichandra Addanki, Peter W. Battaglia, David Budden, Andreea Deac, Jonathan Godwin, Thomas Keck, Wai Lok Sibon Li, Alvaro Sanchez-Gonzalez, Jacklynn Stott, Shantanu Thakoor, Petar Velickovic:
Large-scale graph representation learning with very deep GNNs and self-supervision. CoRR abs/2107.09422 (2021) - [i4]Michael Schaarschmidt, Dominik Grewe, Dimitrios Vytiniotis, Adam Paszke, Georg Stefan Schmid, Tamara Norman, James Molloy, Jonathan Godwin, Norman Alexander Rink, Vinod Nair, Dan Belov:
Automap: Towards Ergonomic Automated Parallelism for ML Models. CoRR abs/2112.02958 (2021) - [i3]Kimberly L. Stachenfeld, Drummond B. Fielding
, Dmitrii Kochkov, Miles D. Cranmer
Learned Coarse Models for Efficient Turbulence Simulation. CoRR abs/2112.15275 (2021) - 2020
- [c1]Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter W. Battaglia:
Learning to Simulate Complex Physics with Graph Networks. ICML 2020: 8459-8468 - [i2]Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter W. Battaglia:
Learning to Simulate Complex Physics with Graph Networks. CoRR abs/2002.09405 (2020)
2010 – 2019
- 2016
- [i1]Jonathan Godwin, Pontus Stenetorp, Sebastian Riedel:
Deep Semi-Supervised Learning with Linguistically Motivated Sequence Labeling Task Hierarchies. CoRR abs/1612.09113 (2016)
Coauthor Index
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