On the implementation of multilevel Monte Carlo simulation of the stochastic volatility and interest rate model using multi-GPU clusters. - Harold A. Lay; Zane Colgin; Viktor Reshniak; Abdul Q. M. Khaliq (Monte Carlo Methods Appl., 2018)

Slow-scale split-step tau-leap method for stiff stochastic chemical systems. - Viktor Reshniak; Abdul Q. M. Khaliq; David A. Voss (J. Comput. Appl. Math., 2019)

Method of Green's Potentials for Elliptic PDEs in Domains with Random Apertures. - Viktor Reshniak; Yuri A. Melnikov (J. Sci. Comput., 2020)

A Nonlocal Feature-Driven Exemplar-Based Approach for Image Inpainting. - Viktor Reshniak; Jeremy Trageser; Clayton G. Webster (SIAM J. Imaging Sci., 2020)

Stable Anderson Acceleration for Deep Learning. - Massimiliano Lupo Pasini; Junqi Yin; Viktor Reshniak; Miroslav Stoyanov (CoRR, 2021)

Robust Learning with Implicit Residual Networks. - Viktor Reshniak; Clayton G. Webster (Mach. Learn. Knowl. Extr., 2021)

Dissipative residual layers for unsupervised implicit parameterization of data manifolds. - Viktor Reshniak (CoRR, 2022)

Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation - A Case Study on Simulation Data with Extreme Climate Events Analysis. - Qian Gong; Chengzhu Zhang; Xin Liang 001; Viktor Reshniak; Jieyang Chen; Anand Rangarajan 001; Sanjay Ranka; Nicolas Vidal 003; Lipeng Wan 001; Paul Ullrich; Norbert Podhorszki; Robert Jacob; et al. (e-Science, 2023)

MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring. - Qian Gong; Jieyang Chen; Ben Whitney; Xin Liang 001; Viktor Reshniak; Tania Banerjee; Jaemoon Lee; Anand Rangarajan 001; Lipeng Wan 001; Nicolas Vidal 003; Qing Liu 002; Ana Gainaru; et al. (SoftwareX, 2023)

Tuning the Interpolation Basis in a Multigrid Decomposition for Local Error Control. - Nicolas Vidal 003; Qian Gong; Viktor Reshniak; Rick Archibald; Scott Klasky (IEEE Big Data, 2024)

A Framework for Compressing Unstructured Scientific Data via Serialization. - Viktor Reshniak; Qian Gong; Rick Archibald; Scott Klasky; Norbert Podhorszki (IEEE Big Data, 2024)

Assessing Membership Inference Attacks under Distribution Shifts. - Yichuan Shi; Olivera Kotevska; Viktor Reshniak; Amir Sadovnik (IEEE Big Data, 2024)

A General Framework for Error-controlled Unstructured Scientific Data Compression. - Qian Gong; Zhe Wang 059; Viktor Reshniak; Xin Liang 001; Jieyang Chen; Qing Liu 002; Tushar M. Athawale; Yi Ju; Anand Rangarajan 001; Sanjay Ranka; Norbert Podhorszki; Rick Archibald; et al. (e-Science, 2024)

Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy. - Yichuan Shi; Olivera Kotevska; Viktor Reshniak; Abhishek Singh 005; Ramesh Raskar (CoRR, 2024)

Lifting MGARD: construction of (pre)wavelets on the interval using polynomial predictors of arbitrary order. - Viktor Reshniak; Evan Ferguson; Qian Gong; Nicolas Vidal 003; Rick Archibald; Scott Klasky (CoRR, 2024)