10 citations · 44 across the 17 of their papers we have counts for
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cs.LG2019
Tiered Graph Autoencoders with PyTorch Geometric for Molecular Graphs
Daniel T. Chang
Tiered latent representations and latent spaces for molecular graphs provide a simple but effective way to explicitly represent and utilize groups (e.g., functional groups), which…
cs.LG2019★ 7 cited
Tiered Latent Representations and Latent Spaces for Molecular Graphs
Daniel T. Chang
Molecular graphs generally contain subgraphs (known as groups) that are identifiable and significant in composition, functionality, geometry, etc. Flat latent representations (node…
cs.LG2019★ 5 cited
Probabilistic Generative Deep Learning for Molecular Design
Daniel T. Chang
Probabilistic generative deep learning for molecular design involves the discovery and design of new molecules and analysis of their structure, properties and activities by probabi…