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Found 3 papers, 1 papers with code
Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction
In computational chemistry, crystal structure prediction (CSP) is an optimization problem that involves discovering the lowest energy stable crystal structure for a given chemical formula.
Contrastive Neural Processes for Self-Supervised Learning
In this paper, we propose a novel self-supervised learning framework that combines contrastive learning with neural processes.
On the Model Shrinkage Effect of Gamma Process Edge Partition Models
In order to ensure that the model shrinkage effect of the EPM works in an appropriate manner, we proposed two novel generative constructions of the EPM: CEPM incorporating constrained gamma priors, and DEPM incorporating Dirichlet priors instead of the gamma priors.
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