Search Results for author:
Found 8 papers, 1 papers with code
Ising on the Graph: Task-specific Graph Subsampling via the Ising Model
Typically, the reduction approaches either remove edges (sparsification) or merge nodes (coarsening) in an unsupervised way with no specific downstream task in mind.
Carbohydrate NMR chemical shift predictions using E(3) equivariant graph neural networks
Nuclear Magnetic Resonance (NMR) spectroscopy plays a crucial role in understanding their intricate molecular arrangements and is essential in assessing and verifying the molecular structure of organic molecules.
Variational Elliptical Processes
We present elliptical processes, a family of non-parametric probabilistic models that subsume Gaussian processes and Student's t processes.
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction
Molecular property prediction is essential in chemistry, especially for drug discovery applications.
Graph-based Neural Acceleration for Nonnegative Matrix Factorization
We describe a graph-based neural acceleration technique for nonnegative matrix factorization that builds upon a connection between matrices and bipartite graphs that is well-known in certain fields, e. g., sparse linear algebra, but has not yet been exploited to design graph neural networks for matrix computations.
The Elliptical Processes: a Family of Fat-tailed Stochastic Processes
We present the elliptical processes -- a family of non-parametric probabilistic models that subsumes the Gaussian process and the Student-t process.
Matrix Multilayer Perceptron
Models that output a vector of responses given some inputs, in the form of a conditional mean vector, are at the core of machine learning.
Constructing the Matrix Multilayer Perceptron and its Application to the VAE
However, in certain scenarios we are interested in learning structured parameters (predictions) in the form of symmetric positive definite matrices.
