2 citations · 3 across the 4 of their papers we have counts for
4 papers
Optimizing Rank for High-Fidelity Implicit Neural Representations
Julian McGinnis, Florian A. Hölzl, Suprosanna Shit +6
Implicit Neural Representations (INRs) based on vanilla Multi-Layer Perceptrons (MLPs) are widely believed to be incapable of representing high-frequency content. This has directed…
Gradient-Weight Alignment as a Train-Time Proxy for Generalization in Classification Tasks
Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
Robust validation metrics remain essential in contemporary deep learning, not only to detect overfitting and poor generalization, but also to monitor training dynamics. In the supe…
Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models
Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
Differentially Private Stochastic Gradient Descent (DP-SGD) limits the amount of private information deep learning models can memorize during training. This is achieved by clipping…
Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis
Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
Machine learning with formal privacy-preserving techniques like Differential Privacy (DP) allows one to derive valuable insights from sensitive medical imaging data while promising…