Search Results for author:
Found 3 papers, 1 papers with code
A unified framework for learning with nonlinear model classes from arbitrary linear samples
In summary, our work not only introduces a unified way to study learning unknown objects from general types of data, but also establishes a series of general theoretical guarantees which consolidate and improve various known results.
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions
Our framework extends the standard setup by allowing for general types of data, rather than merely pointwise samples of the target function.
CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning
In this work, we propose an adaptive sampling strategy, CAS4DL (Christoffel Adaptive Sampling for Deep Learning) to increase the sample efficiency of DL for multivariate function approximation.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
