31 citations · 50 across the 5 of their papers we have counts for
6 papers
Data Free Training of Diffusion Models for Lattice Field Theories
Dennis Grinwald, Ankur Singha, Khaled Kahouli +2
Sampling equilibrium field configurations remains a major computational bottleneck in lattice field theory, motivating the development of generative samplers that can produce indep…
Carbon-Aware Quality Adaptation for Energy-Intensive Services
Philipp Wiesner, Dennis Grinwald, Philipp Weiß +3
The energy demand of modern cloud services, particularly those related to generative AI, is increasing at an unprecedented pace. To date, carbon-aware computing strategies have pri…
Federated Learning over Connected Modes
Dennis Grinwald, Philipp Wiesner, Shinichi Nakajima
Statistical heterogeneity in federated learning poses two major challenges: slow global training due to conflicting gradient signals, and the need of personalization for local dist…
FedZero: Leveraging Renewable Excess Energy in Federated Learning
Philipp Wiesner, Ramin Khalili, Dennis Grinwald +3
Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing. Yet, FL inevitably…
DORA: Exploring Outlier Representations in Deep Neural Networks
Kirill Bykov, Mayukh Deb, Dennis Grinwald +2
Deep Neural Networks (DNNs) excel at learning complex abstractions within their internal representations. However, the concepts they learn remain opaque, a problem that becomes par…
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks
Dennis Grinwald, Kirill Bykov, Shinichi Nakajima +1
Explainable Artificial Intelligence (XAI) aims to make learning machines less opaque, and offers researchers and practitioners various tools to reveal the decision-making strategie…