A Convergence Rate for Manifold Neural Networks
arXiv preprint, 2022
Recommended citation: Chew, J.A.; Perlmutter, M.; Needell, D. A Convergence Rate for Manifold Neural Networks. arXiv preprint, 2022. Online access
Publications
Multi-scale Hybridized Topic Modeling: A Pipeline for Analyzing Unstructured Text Datasets using Topic Modeling
arXiv preprint, 2022
Recommended citation: Cheng, K; Inzer, S.; Leung, A.; Shen, X.; Perlmutter, M.; Lindstrom, M.; Chew, J.A.; Presner, T.; Needell, D. Multi-scale Hybridized Topic Modeling: A Pipeline for Analyzing Unstructured Text Datasets using Topic Modeling. Submitted for publication, 2022. Online access
Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data
Analytica Chimica Acta, 2022
Recommended citation: Kazmierczak, N.P.; Chew, J.A.; Vander Griend, D.A. Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data. Analytica Chimica Acta, 2022, 1227:339834. Online access
Geometric Scattering on Measure Spaces
arXiv preprint, 2022
Recommended citation: Chew, J.A.; Hirn, M.; Krishnaswamy, S.; Needell, D.; Perlmutter, M.; Steach, H.R.; Viswanath, S.; Wu, H.-T. Geometric Scattering on Measure Spaces. Submitted for publication, 2022. Online access
The Manifold Scattering Transform for High-Dimensional Point Cloud Data
ICML Workshop on Topology, Algebra, and Geometry in Machine Learning, 2022
Recommended citation: Chew, J.A.; Steach, H.R.; Viswanath, S.; Wu, H.-T.; Hirn, M.; Needell, D.; Krishnaswamy, S.; Perlmutter, M. The Manifold Scattering Transform for High-Dimensional Point Cloud Data. arXiv preprint, 2022. Online access
A reliable algorithm for calculating stoichiometry parameters in the hard modeling of spectrophotometric titration data
Journal of Chemometrics, 2022
Recommended citation: Kazmierczak, N.P.; Chew, J.A.; Vander Griend, D.A. A reliable algorithm for calculating stoichiometry parameters in the hard modeling of spectrophotometric titration data. Journal of Chemometrics, 2022, 36:e3409. Online access
Guided Semi-Supervised Non-Negative Matrix Factorization
Algorithms, 2022
Recommended citation: Li, P.; Tseng, C.; Zheng, Y.; Chew, J.A.; Huang, L.; Jarman, B.; Needell, D. Guided Semi-Supervised Non-Negative Matrix Factorization. Algorithms 2022, 15:136 Online access
Sensitivity Limits for Determining 1:1 Binding Constants from Spectrophotometric Titrations via Global Analysis
Journal of Chemometrics, 2019
Recommended citation: Kazmierczak, N.P.; Chew, J.A.; Michmerhuizen, A.R.; Kim, S.E.; Drees, Z.D.; Rylaarsdam, A.; Thong, T.; Van Laar, L.; Vander Griend, D.A. Sensitivity Limits for Determining 1:1 Binding Constants from Spectrophotometric Titrations via Global Analysis. Journal of Chemometrics, 2019, 33:e3119. Online access