Society for Industrial and Applied Mathematics (SIAM) recently launched SIAM Journal on Mathematics of Data Science (SIMODS), which began publishing work in 2019. Read a SIMODS top articles and then consider submitting a paper of your own.
SIAM Journal on Mathematics of Data Science
SIMODS publishes work that advances mathematical, statistical, and computational methods in the context of data and information sciences.
Click below to read the most frequently downloaded articles in 2019
1. Why Are Big Data Matrices Approximately Low Rank? (Madeleine Udell and Alex Townsend)
2. Optimal Approximation with Sparsely Connected Deep Neural Networks (Helmut Bolcskei, Philipp Grohs, Gitta Kutyniok, and Phillipp Petersen)
3. New Error Bounds for Deep ReLU Networks Using Sparse Grids (Hadrien Montanelli and Qiang Du)
4. The Rankability of Data (Paul Anderson, Timothy Chartier, and Amy Langville)
5. Multi-Layer Sparse Coding: The Holistic Way (Aviad Aberdam, Jeremias Sulam, and Michael Elad)
6. Gaussian Process Landmarking on Manifolds (Tingran Gao, Shahar Z. Kovalsky, and Ingrid Daubechies)
7 Gaussian Process Landmarking for Three-Dimensional Geometric Morphometrics (Tingran Gao, Shahar Z. Kovalsky, Doug M. Boyer, and Ingrid Daubechies)
8. Clustering with t-SNE, Provably (George C. Linderman and Stefan Steinerberger)
9. Sequential Sampling for Optimal Weighted Least Squares Approximations in Hierarchical Spaces (Benjamin Arras, Markus Bachmayr, and Albert Cohen)
10. Decoding from Pooled Data: Sharp Information -Theoretic Bounds (Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, and Michael I. Jordan)
Is your work relevant to mathematical, statistical, and computational methods in the context of data science? Submit your next manuscript to SIMODS here.
Learn more about SIAM Journals.