102 citations · 787 across the 61 of their papers we have counts for
9 papers · 1 filter
Quadrature Sampling of Parametric Models with Bi-fidelity Boosting
Nuojin Cheng, Osman Asif Malik, Yiming Xu +3
Least squares regression is a ubiquitous tool for building emulators (a.k.a. surrogate models) of problems across science and engineering for purposes such as design space explorat…
Fast algorithms for least square problems with Kronecker lower subsets
Osman Asif Malik, Yiming Xu, Nuojin Cheng +3
While leverage score sampling provides powerful tools for approximating solutions to large least squares problems, the cost of computing exact scores and sampling often prohibits p…
Nonparametric Embeddings of Sparse High-Order Interaction Events
Zheng Wang, Yiming Xu, Conor Tillinghast +3
High-order interaction events are common in real-world applications. Learning embeddings that encode the complex relationships of the participants from these events is of great imp…
Variational Inference for Nonlinear Inverse Problems via Neural Net Kernels: Comparison to Bayesian Neural Networks, Application to Topology Optimization
Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan
Inverse problems and, in particular, inferring unknown or latent parameters from data are ubiquitous in engineering simulations. A predominant viewpoint in identifying unknown para…
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs
Justin Baker, Hedi Xia, Yiwei Wang +7
Learning neural ODEs often requires solving very stiff ODE systems, primarily using explicit adaptive step size ODE solvers. These solvers are computationally expensive, requiring…
Weight Matrix Dimensionality Reduction in Deep Learning via Kronecker Multi-layer Architectures
Jarom D. Hogue, Robert M. Kirby, Akil Narayan
Deep learning using neural networks is an effective technique for generating models of complex data. However, training such models can be expensive when networks have large model c…