10 Most Read Data Science Published Papers from SIMODS Vol. 2

Do you know about SIAM Journal on the Mathematics of Data Science (SIMODS)? 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 from Volume 2 of the publication, available to all through December 2020.

• Layer-Parallel Training of Deep Residual Neural Networks (Stefanie Günther, Lars Ruthotto, Jacob B. Schroder, Eric C. Cyr, and Nicolas R. Gauger) 
• A Bayesian Framework for Persistent Homology (Vasileios Maroulas, Farzana Nasrin, and Christopher Oballe) 
• On Gradient-Based Learning in Continuous Games (Eric Mazumdar, Lillian J. Ratliff, and S. Shankar Sastry)
• Graph Powering and Spectral Robustness (Emmanuel Abbe, Enric Boix-Adserà, Peter Ralli, and Colin Sandon)
• Accelerated Gossip in Networks of Given Dimension Using Jacobi Polynomial Iterations (Raphaël Berthier, Francis Bach, and Pierre Gaillard)
• Fast Convex Pruning of Deep Neural Networks (Alireza Aghasi, Afshin Abdi, and Justin Romberg)
• On a Minimum Distance Procedure for Threshold Selection in Tail Analysis (Holger Drees, Anja Janßen , Sidney I. Resnick, and Tiandong Wang) 
• Randomized Algorithms for Low-Rank Tensor Decompositions in the Tucker Format (Rachel Minster, Arvind K. Saibaba, and Misha E. Kilmer) 
• Geometry and Symmetry in Short-and-Sparse Deconvolution (Han-Wen Kuo , Yuqian Zhang, Yenson Lau, and John Wright)
• Detecting Overlapping Communities in Networks Using Spectral Methods (Yuan Zhang, Elizaveta Levina, and Ji Zhu)

Is your work relevant to mathematical, statistical, and computational methods in the context of data science? Submit your next manuscript to SIMODS here.