3 papers
cs.LG2026
Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification
Jack Michael Solomon, Rishi Leburu, Matthias Chung
Inverse problems are fundamental to many scientific and engineering disciplines; they arise when one seeks to reconstruct hidden, underlying quantities from noisy measurements. Man…
cs.LG2024
Sparse -Autoencoders for Scientific Data Compression
Matthias Chung, Rick Archibald, Paul Atzberger +1
Scientific datasets present unique challenges for machine learning-driven compression methods, including more stringent requirements on accuracy and mitigation of potential invalid…
math.NA2023
Image reconstructions using sparse dictionary representations and implicit, non-negative mappings
Elizabeth Newman, Jack Michael Solomon, Matthias Chung
Many imaging science tasks can be modeled as a discrete linear inverse problem. Solving linear inverse problems is often challenging, with ill-conditioned operators and potentially…