30 citations · 76 across the 6 of their papers we have counts for
4 papers · 1 filter
Automap: Towards Ergonomic Automated Parallelism for ML Models
Michael Schaarschmidt, Dominik Grewe, Dimitrios Vytiniotis +8
The rapid rise in demand for training large neural network architectures has brought into focus the need for partitioning strategies, for example by using data, model, or pipeline…
Learned Coarse Models for Efficient Turbulence Simulation
Kimberly Stachenfeld, Drummond B. Fielding, Dmitrii Kochkov +7
Turbulence simulation with classical numerical solvers requires high-resolution grids to accurately resolve dynamics. Here we train learned simulators at low spatial and temporal r…
Large-scale graph representation learning with very deep GNNs and self-supervision
Ravichandra Addanki, Peter W. Battaglia, David Budden +8
Effectively and efficiently deploying graph neural networks (GNNs) at scale remains one of the most challenging aspects of graph representation learning. Many powerful solutions ha…
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
Jonathan Godwin, Michael Schaarschmidt, Alexander Gaunt +5
In this paper we show that simple noise regularisation can be an effective way to address GNN oversmoothing. First we argue that regularisers addressing oversmoothing should both p…