24 citations · 35 across the 9 of their papers we have counts for
5 papers · 1 filter
Applications of Lipschitz neural networks to the Run 3 LHCb trigger system
Blaise Delaney, Nicole Schulte, Gregory Ciezarek +3
The operating conditions defining the current data taking campaign at the Large Hadron Collider, known as Run 3, present unparalleled challenges for the real-time data acquisition…
DiSK: A Diffusion Model for Structured Knowledge
Ouail Kitouni, Niklas Nolte, James Hensman +1
Structured (dictionary-like) data presents challenges for left-to-right language models, as they can struggle with structured entities for a wide variety of reasons such as formatt…
NuCLR: Nuclear Co-Learned Representations
Ouail Kitouni, Niklas Nolte, Sokratis Trifinopoulos +2
We introduce Nuclear Co-Learned Representations (NuCLR), a deep learning model that predicts various nuclear observables, including binding and decay energies, and nuclear charge r…
Expressive Monotonic Neural Networks
Ouail Kitouni, Niklas Nolte, Michael Williams
The monotonic dependence of the outputs of a neural network on some of its inputs is a crucial inductive bias in many scenarios where domain knowledge dictates such behavior. This…
Development of the Topological Trigger for LHCb Run 3
Nicole Schulte, Blaise Raheem Delaney, Niklas Nolte +3
The data-taking conditions expected in Run 3 of the LHCb experiment at CERN are unprecedented and challenging for the software and computing systems. Despite that, the LHCb collabo…