Jonathan Godwin | DeepAI

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Featured Co-authors

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∙ 09/12/2023

Band-gap regression with architecture-optimized message-passing neural networks

Graph-based neural networks and, specifically, message-passing neural ne...

0 Tim Bechtel, et al. ∙

∙ 09/26/2022

Learned Force Fields Are Ready For Ground State Catalyst Discovery

We present evidence that learned density functional theory (“DFT”) force...

0 Michael Schaarschmidt, et al. ∙

∙ 05/31/2022

Pre-training via Denoising for Molecular Property Prediction

Many important problems involving molecular property prediction from 3D ...

0 Sheheryar Zaidi, et al. ∙

∙ 12/31/2021

Learned Coarse Models for Efficient Turbulence Simulation

Turbulence simulation with classical numerical solvers requires very hig...

5 Kimberly Stachenfeld, et al. ∙

∙ 12/06/2021

Automap: Towards Ergonomic Automated Parallelism for ML Models

The rapid rise in demand for training large neural network architectures...

8 Michael Schaarschmidt, et al. ∙

∙ 07/20/2021

Large-scale graph representation learning with very deep GNNs and self-supervision

Effectively and efficiently deploying graph neural networks (GNNs) at sc...

0 Ravichandra Addanki, et al. ∙

∙ 06/15/2021

Very Deep Graph Neural Networks Via Noise Regularisation

Graph Neural Networks (GNNs) perform learned message passing over an inp...

0 Jonathan Godwin, et al. ∙

∙ 12/31/2020

Graph Networks with Spectral Message Passing

Graph Neural Networks (GNNs) are the subject of intense focus by the mac...

0 Kimberly Stachenfeld, et al. ∙

∙ 02/21/2020

Learning to Simulate Complex Physics with Graph Networks

Here we present a general framework for learning simulation, and provide...

26 Alvaro Sanchez-Gonzalez, et al. ∙

∙ 12/29/2016

Deep Semi-Supervised Learning with Linguistically Motivated Sequence Labeling Task Hierarchies

In this paper we present a novel Neural Network algorithm for conducting...

0 Jonathan Godwin, et al. ∙

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