4 citations · 9 across the 5 of their papers we have counts for
7 papers
Empirical Evidence for Simply Connected Decision Regions in Image Classifiers
Arjhun Swaminathan, Mete Akgün
The topology of a classifier's decision regions determines how inputs with the same predicted label can be connected and deformed without changing that prediction. Prior empirical…
Understanding the Resource Cost of Fully Homomorphic Encryption in Quantum Federated Learning
Lukas Böhm, Arjhun Swaminathan, Anika Hannemann +1
Quantum Federated Learning (QFL) enables distributed training of Quantum Machine Learning (QML) models by sharing model gradients instead of raw data. However, these gradients can…
Accelerating Targeted Hard-Label Adversarial Attacks in Low-Query Black-Box Settings
Arjhun Swaminathan, Mete Akgün
Deep neural networks for image classification remain vulnerable to adversarial examples -- small, imperceptible perturbations that induce misclassifications. In black-box settings,…
Private, Efficient and Scalable Kernel Learning for Medical Image Analysis
Anika Hannemann, Arjhun Swaminathan, Ali Burak Ünal +1
Medical imaging is key in modern medicine. From magnetic resonance imaging (MRI) to microscopic imaging for blood cell detection, diagnostic medical imaging reveals vital insights…
PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies
Arjhun Swaminathan, Anika Hannemann, Ali Burak Ünal +2
Genome-wide association studies are pivotal in understanding the genetic underpinnings of complex traits and diseases. Collaborative, multi-site GWAS aim to enhance statistical pow…
Distributed and Secure Kernel-Based Quantum Machine Learning
Arjhun Swaminathan, Mete Akgün
Quantum computing promises to revolutionize machine learning, offering significant efficiency gains in tasks such as clustering and distance estimation. Additionally, it provides e…
A Privacy-Preserving Federated Learning Approach for Kernel methods
Anika Hannemann, Ali Burak Ünal, Arjhun Swaminathan +2
It is challenging to implement Kernel methods, if the data sources are distributed and cannot be joined at a trusted third party for privacy reasons. It is even more challenging, i…