64 citations · 478 across the 63 of their papers we have counts for
66 papers
Variational Garrote for Statistical Physics-based Sparse and Robust Variable Selection
Hyungjoon Soh, Dongha Lee, Vipul Periwal +1
Selecting key variables from high-dimensional data is increasingly important in the era of big data. Sparse regression serves as a powerful tool for this purpose by promoting model…
Efficient and stable derivative-free Steffensen algorithm for root finding
Alexandre Wagemakers, Vipul Periwal
We explore a family of numerical methods, based on the Steffensen divided difference iterative algorithm, that do not evaluate the derivative of the objective functions. The family…
Multiplicative learning from observation-prediction ratios
Han Kim, Hyungjoon Soh, Vipul Periwal +1
Additive parameter updates, as used in gradient descent and its adaptive extensions, underpin most modern machine-learning optimization. Yet, such additive schemes often demand num…
Topological approach to void finding applied to the SDSS galaxy map
Manu Aggarwal, Motonari Tonegawa, Stephen Appleby +2
The structure of the low redshift Universe is dominated by a multi-scale void distribution delineated by filaments and walls of galaxies. The characteristics of voids; such as morp…
Annealing approach to root-finding
Junghyo Jo, Alexandre Wagemakers, Vipul Periwal
The Newton-Raphson method is a fundamental root-finding technique with numerous applications in physics. In this study, we propose a parameterized variant of the Newton-Raphson met…
Tight basis cycle representatives for persistent homology of large data sets
Manu Aggarwal, Vipul Periwal
Persistent homology (PH) is a popular tool for topological data analysis that has found applications across diverse areas of research. It provides a rigorous method to compute robu…