5 papers
Multigrid Training for Molecular Generation using Graph Neural Networks
Zixuan Ling, Paula Mercurio, Di Liu
Deep learning has demonstrated significant success for modeling biochemical molecular systems, where inputs are commonly represented as graphs or 3D grids. A major challenge is tha…
Clustering Molecular Energy Landscapes by Adaptive Network Embedding
Paula Mercurio, Di Liu
In order to efficiently explore the chemical space of all possible small molecules, a common approach is to compress the dimension of the system to facilitate downstream machine le…
Network Embedding Using Sparse Approximations of Random Walks
Paula Mercurio, Di Liu
In this paper, we propose an efficient numerical implementation of Network Embedding based on commute times, using sparse approximation of a diffusion process on the network obtain…
Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques
Paula Mercurio, Di Liu
Using random walk sampling methods for feature learning on networks, we develop a method for generating low-dimensional node embeddings for directed graphs and identifying transiti…
Universal Gröbner Bases of Toric Ideals of Combinatorial Neural Codes
Melissa Beer, Robert Davis, Thomas Elgin +5
In the 1970s, O'Keefe and Dostrovsky discovered that certain neurons, called place cells, in an animal's brain are tied to its location within its arena. A combinatorial neural cod…