9 citations · 10 across the 4 of their papers we have counts for
4 papers
Better Queries, Cheaper Attention: Adapting Transformers for Efficient Sparse Reconstruction
Philippa Duckett, Samuel Van Stroud, Max Hart +2
Query-based transformer decoders are effective for object reconstruction from sparse scientific sensor measurements, but their scalability to high-multiplicity data is limited by f…
Language hooks: a modular framework for augmenting LLM reasoning that decouples tool usage from the model and its prompt
Damien de Mijolla, Wen Yang, Philippa Duckett +2
Prompting and fine-tuning have emerged as two competing paradigms for augmenting language models with new capabilities, such as the use of tools. Prompting approaches are quick to…
Transformers for Charged Particle Track Reconstruction in High Energy Physics
Samuel Van Stroud, Philippa Duckett, Max Hart +4
Reconstructing charged particle tracks is a fundamental task in modern collider experiments. The unprecedented particle multiplicities expected at the High-Luminosity Large Hadron…
Reconstructing charged particle track segments with a quantum-enhanced support vector machine
Philippa Duckett, Gabriel Facini, Marcin Jastrzebski +3
Reconstructing the trajectories of charged particles from the collection of hits they leave in the detectors of collider experiments like those at the Large Hadron Collider (LHC) i…