175 citations · 317 across the 46 of their papers we have counts for
5 papers · 1 filter
BelNet: Basis enhanced learning, a mesh-free neural operator
Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer
Operator learning trains a neural network to map functions to functions. An ideal operator learning framework should be mesh-free in the sense that the training does not require a…
Random Feature Models for Learning Interacting Dynamical Systems
Yuxuan Liu, Scott G. McCalla, Hayden Schaeffer
Particle dynamics and multi-agent systems provide accurate dynamical models for studying and forecasting the behavior of complex interacting systems. They often take the form of a…
Concentration of Random Feature Matrices in High-Dimensions
Zhijun Chen, Hayden Schaeffer, Rachel Ward
The spectra of random feature matrices provide essential information on the conditioning of the linear system used in random feature regression problems and are thus connected to t…
SRMD: Sparse Random Mode Decomposition
Nicholas Richardson, Hayden Schaeffer, Giang Tran
Signal decomposition and multiscale signal analysis provide many useful tools for time-frequency analysis. We proposed a random feature method for analyzing time-series data by con…
HARFE: Hard-Ridge Random Feature Expansion
Esha Saha, Hayden Schaeffer, Giang Tran
We propose a random feature model for approximating high-dimensional sparse additive functions called the hard-ridge random feature expansion method (HARFE). This method utilizes a…