38 citations · 58 across the 29 of their papers we have counts for
7 papers · 1 filter
Skillful and realistic precipitation forecasting with reinforced diffusion
Ozan Sener, Anand Avati, David Hafner +7
Accurate precipitation forecasting remains elusive as physics-based models struggle to resolve mesoscale convection and machine learning often yields physically inconsistent, blurr…
Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning
Mirko Konstantin, Stefan Zachow, Anirban Mukhopadhyay
Federated learning (FL) enables collaborative model training across distributed clients while keeping data local. A central challenge is determining which client updates are benefi…
ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learning
Mirko Konstantin, Moritz Fuchs, Anirban Mukhopadhyay
Federated Learning (FL) allows the training of deep neural networks in a distributed and privacy-preserving manner. However, this concept suffers from malfunctioning updates sent b…
Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning
Mirko Konstantin, Anirban Mukhopadhyay
Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy by keeping data local. Traditional FL approaches rely on a cen…
Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity
Niklas Babendererde, Haozhe Zhu, Moritz Fuchs +2
Federated- and Continual Learning have been established as approaches to enable privacy-aware learning on continuously changing data, as required for deploying AI systems in histop…
Unsupervised Training of Neural Cellular Automata on Edge Devices
John Kalkhof, Amin Ranem, Anirban Mukhopadhyay
The disparity in access to machine learning tools for medical imaging across different regions significantly limits the potential for universal healthcare innovation, particularly…
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Sarthak Pati, Siddhesh P. Thakur, İbrahim Ethem Hamamcı +39
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms,…