Viktor Reshniak

According to our database, Viktor Reshniak authored at least 13 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A framework for compressing unstructured scientific data via serialization.

Viktor Reshniak, Qian Gong, Rick Archibald, Scott Klasky, Norbert Podhorszki

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, Rick Archibald, Scott Klasky

CoRR, 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, Ramesh Raskar

CoRR, 2024

A General Framework for Error-controlled Unstructured Scientific Data Compression.

Qian Gong, Zhe Wang, Viktor Reshniak, Xin Liang, Jieyang Chen, Qing Liu, Tushar M. Athawale, Yi Ju, Anand Rangarajan, Sanjay Ranka, Norbert Podhorszki, Rick Archibald, Scott Klasky

Proceedings of the 20th IEEE International Conference on e-Science, 2024

2023

MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring.

Qian Gong, Jieyang Chen, Ben Whitney, Xin Liang, Viktor Reshniak, Tania Banerjee, Jaemoon Lee, Anand Rangarajan, Lipeng Wan, Nicolas Vidal, Qing Liu, Ana Gainaru, Norbert Podhorszki, Richard Archibald, Sanjay Ranka, Scott Klasky

SoftwareX, December, 2023

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, Viktor Reshniak, Jieyang Chen, Anand Rangarajan, Sanjay Ranka, Nicolas Vidal, Lipeng Wan, Paul Ullrich, Norbert Podhorszki, Robert Jacob, Scott Klasky

Proceedings of the 19th IEEE International Conference on e-Science, 2023

2022

Dissipative residual layers for unsupervised implicit parameterization of data manifolds.

Viktor Reshniak

CoRR, 2022

2021

Robust Learning with Implicit Residual Networks.

Viktor Reshniak, Clayton G. Webster

Mach. Learn. Knowl. Extr., 2021

Stable Anderson Acceleration for Deep Learning.

Massimiliano Lupo Pasini, Junqi Yin, Viktor Reshniak, Miroslav Stoyanov

CoRR, 2021

2020

A Nonlocal Feature-Driven Exemplar-Based Approach for Image Inpainting.

Viktor Reshniak, Jeremy Trageser, Clayton G. Webster

SIAM J. Imaging Sci., 2020

Method of Green's Potentials for Elliptic PDEs in Domains with Random Apertures.

Viktor Reshniak, Yuri A. Melnikov

J. Sci. Comput., 2020

2019

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

2018

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