no code implementations • 6 Jun 2023 • Apurva Gandhi, Thong Q. Nguyen, Huitian Jiao, Robert Steen, Ameya Bhatawdekar
We present Semantic Interpreter, a natural language-friendly AI system for productivity software such as Microsoft Office that leverages large language models (LLMs) to execute user intent across application features.
no code implementations • 5 Oct 2020 • Cheng Chen, Olmo Cerri, Thong Q. Nguyen, Jean-Roch Vlimant, Maurizio Pierini
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets.
1 code implementation • 4 May 2020 • Oliver Knapp, Guenther Dissertori, Olmo Cerri, Thong Q. Nguyen, Jean-Roch Vlimant, Maurizio Pierini
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider.
3 code implementations • 26 Sep 2019 • Eric A. Moreno, Thong Q. Nguyen, Jean-Roch Vlimant, Olmo Cerri, Harvey B. Newman, Avikar Periwal, Maria Spiropulu, Javier M. Duarte, Maurizio Pierini
We develop a jet identification algorithm based on an interaction network, designed to identify high-momentum Higgs bosons decaying to bottom quark-antiquark pairs, distinguish them from ordinary jets originating from the hadronization of quarks and gluons.
High Energy Physics - Experiment High Energy Physics - Phenomenology
2 code implementations • 14 Aug 2019 • Eric A. Moreno, Olmo Cerri, Javier M. Duarte, Harvey B. Newman, Thong Q. Nguyen, Avikar Periwal, Maurizio Pierini, Aidana Serikova, Maria Spiropulu, Jean-Roch Vlimant
We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons.
High Energy Physics - Experiment High Energy Physics - Phenomenology
1 code implementation • 26 Nov 2018 • Olmo Cerri, Thong Q. Nguyen, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant
Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events.
3 code implementations • 29 Jun 2018 • Thong Q. Nguyen, Daniel Weitekamp III, Dustin Anderson, Roberto Castello, Olmo Cerri, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant
We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider.