7 citations · 16 across the 4 of their papers we have counts for
8 papers
Distance-Geometric Graph Convolutional Network (DG-GCN) for Three-Dimensional (3D) Graphs
Daniel T. Chang
The distance-geometric graph representation adopts a unified scheme (distance) for representing the geometry of three-dimensional(3D) graphs. It is invariant to rotation and transl…
Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on Three-Dimensional (3D) Graphs
Daniel T. Chang
The geometry of three-dimensional (3D) graphs, consisting of nodes and edges, plays a crucial role in many important applications. An excellent example is molecular graphs, whose g…
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…
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…
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…
Latent Variable Modeling for Generative Concept Representations and Deep Generative Models
Daniel T. Chang
Latent representations are the essence of deep generative models and determine their usefulness and power. For latent representations to be useful as generative concept representat…