1 citations · 3 across the 5 of their papers we have counts for
5 papers
Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions
Junyang Cai, Khai-Nguyen Nguyen, Nishant Shrestha +5
One surprising trait of neural networks is the extent to which their connections can be pruned with little to no effect on accuracy. But when we cross a critical level of parameter…
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…
Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition
Zheng Wang, Shandian Zhe
Tensor decomposition is a fundamental framework to analyze data that can be represented by multi-dimensional arrays. In practice, tensor data is often accompanied by temporal infor…
Infinite-Fidelity Coregionalization for Physical Simulation
Shibo Li, Zheng Wang, Robert M. Kirby +1
Multi-fidelity modeling and learning are important in physical simulation-related applications. It can leverage both low-fidelity and high-fidelity examples for training so as to r…
GP-HMAT: Scalable, Gaussian Process Regression with Hierarchical Low-Rank Matrices
Vahid Keshavarzzadeh, Shandian Zhe, Robert M. Kirby +1
A Gaussian process (GP) is a powerful and widely used regression technique. The main building block of a GP regression is the covariance kernel, which characterizes the relationshi…