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Found 4 papers, 2 papers with code
Learning Regularized Positional Encoding for Molecular Prediction
To model the complex nonlinearity in predicting molecular properties in an more end-to-end approach, we propose to encode the positional quantities with a learnable embedding that is continuous and differentiable.
Supervised Pretraining for Molecular Force Fields and Properties Prediction
Experiments show that, compared to training from scratch, fine-tuning the pretrained model can significantly improve the performance for seven molecular property prediction tasks and two force field tasks.
Deep Retrieval: An End-to-End Structure Model for Large-Scale Recommendations
With the model learnt, a beam search over the latent codes is performed to retrieve the top candidates.
Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations
With the model learnt, a beam search over the structure is performed to retrieve the top candidates for reranking.
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