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cs.LG2025
Towards a Physics Foundation Model
Florian Wiesner, Zoë J. Gray, Matthias Wessling +1
Foundation models have revolutionized natural language processing through a ``train once, deploy anywhere'' paradigm, where a single pre-trained model adapts to countless downstrea…
cs.LG2024
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning
Brianna Mueller, W. Nick Street, Stephen Baek +3
Federated learning (FL) enables multiple clients with distributed data sources to collaboratively train a shared model without compromising data privacy. However, existing FL parad…
cs.LG2024
Constrained Synthesis with Projected Diffusion Models
Jacob K Christopher, Stephen Baek, Ferdinando Fioretto
This paper introduces an approach to endow generative diffusion processes the ability to satisfy and certify compliance with constraints and physical principles. The proposed metho…