4 papers
GaugeQuant: Online Learning of Quantization-Optimal Bases from LLM Symmetries
Miguel P. Bento, João Seabra, João F. Seabra
Transformers are known to have internal continuous symmetries that leave outputs invariant, while modifying quantization. GaugeQuant leverages this in-training by introducing a Log…
Solving stiff dark matter equations via Jacobian Normalization with Physics-Informed Neural Networks
M. P. Bento, H. B. Câmara, J. R. Rocha +1
Stiff differential equations pose a major challenge for Physics-Informed Neural Networks (PINNs), often causing poor convergence. We propose a simple, hyperparameter-free method to…
MUSE: Multi-Tenant Model Serving With Seamless Model Updates
Cláudio Correia, Alberto E. A. Ferreira, Lucas Martins +7
In binary classification systems, decision thresholds translate model scores into actions. Choosing suitable thresholds relies on the specific distribution of the underlying model…
Unraveling particle dark matter with Physics-Informed Neural Networks
M. P. Bento, H. B. Câmara, J. F. Seabra
We parametrically solve the Boltzmann equations governing freeze-in dark matter (DM) in alternative cosmologies with Physics-Informed Neural Networks (PINNs), a mesh-free method. T…