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Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning
Neural Information Processing Systems
We design efficient distance approximation algorithms for several classes of well-studied structured high-dimensional distributions.
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884d247c6f65a96a7da4d1105d584ddd-Supplemental.pdf
Neural Information Processing Systems
To push the boundaries of molecular representation learning, we present PhysChem, a novel neural architecture that learns molecular representations via fusing physical and chemical information of molecules.
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.90)