2022
DOI: 10.48550/arxiv.2211.04598
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Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators

Abstract: The demonstrated success of transfer learning has popularized approaches that involve pretraining models from massive data sources and subsequent finetuning towards a specific task. While such approaches have become the norm in fields such as natural language processing, implementation and evaluation of transfer learning approaches for chemistry are in the early stages. In this work, we demonstrate finetuning for downstream tasks on a graph neural network (GNN) trained over a molecular database containing 2.7 … Show more

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