16 citations · 19 across the 4 of their papers we have counts for
4 papers
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy
Yichuan Shi, Olivera Kotevska, Viktor Reshniak +2
Federated Learning (FL) has emerged as a leading paradigm for decentralized, privacy preserving machine learning training. However, recent research on gradient inversion attacks (G…
Dissipative residual layers for unsupervised implicit parameterization of data manifolds
Viktor Reshniak
We propose an unsupervised technique for implicit parameterization of data manifolds. In our approach, the data is assumed to belong to a lower dimensional manifold in a higher dim…
A nonlocal feature-driven exemplar-based approach for image inpainting
Viktor Reshniak, Jeremy Trageser, Clayton G. Webster
We present a nonlocal variational image completion technique which admits simultaneous inpainting of multiple structures and textures in a unified framework. The recovery of geomet…
Robust learning with implicit residual networks
Viktor Reshniak, Clayton Webster
In this effort, we propose a new deep architecture utilizing residual blocks inspired by implicit discretization schemes. As opposed to the standard feed-forward networks, the outp…