Adrian Alan Pol

According to our database, Adrian Alan Pol authored at least 14 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Measuring the Groundedness of Legal Question-Answering Systems.

Dietrich Trautmann, Natalia Ostapuk, Quentin Grail, Adrian Alan Pol, Guglielmo Bonifazi, Shang Gao, Martin Gajek

CoRR, 2024

2023

Symbolic Regression on FPGAs for Fast Machine Learning Inference.

Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar, Ekaterina Govorkova, Miles D. Cranmer, Sridhara Dasu, Peter Elmer, Philip C. Harris, Isobel Ojalvo, Maurizio Pierini

CoRR, 2023

Towards Optimal Compression: Joint Pruning and Quantization.

Ben Zandonati, Glenn Bucagu, Adrian Alan Pol, Maurizio Pierini, Olya Sirkin, Tal Kopetz

CoRR, 2023

FIT: A Metric for Model Sensitivity.

Ben Zandonati, Adrian Alan Pol, Maurizio Pierini, Olya Sirkin, Tal Kopetz

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Knowledge Distillation for Anomaly Detection.

Adrian Alan Pol, Ekaterina Govorkova, Sonja Grönroos, Nadezda Chernyavskaya, Philip C. Harris, Maurizio Pierini, Isobel Ojalvo, Peter Elmer

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

2022

Author Correction: Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider.

Ekaterina Govorkova, Ema Puljak, Thea Aarrestad, Thomas James, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Nicolò Ghielmetti, Maksymilian Graczyk, Sioni Summers, Jennifer Ngadiuba, Thong Q. Nguyen, Javier M. Duarte, Zhenbin Wu

Nat. Mach. Intell., 2022

Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider.

Ekaterina Govorkova, Ema Puljak, Thea Aarrestad, Thomas James, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Nicolò Ghielmetti, Maksymilian Graczyk, Sioni Summers, Jennifer Ngadiuba, Thong Q. Nguyen, Javier M. Duarte, Zhenbin Wu

Nat. Mach. Intell., 2022

Lightweight jet reconstruction and identification as an object detection task.

Adrian Alan Pol, Thea Aarrestad, Ekaterina Govorkova, Roi Halily, Anat Klempner, Tal Kopetz, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Olya Sirkin, Sioni Summers

Mach. Learn. Sci. Technol., 2022

2021

Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors.

Claudionor José Nunes Coelho Jr., Aki Kuusela, Shan Li, Hao Zhuang, Jennifer Ngadiuba, Thea Klaeboe Aarrestad, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Sioni Summers

Nat. Mach. Intell., 2021

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices.

Farah Fahim, Benjamin Hawks, Christian Herwig, James Hirschauer, Sergo Jindariani, Nhan Tran, Luca P. Carloni, Giuseppe Di Guglielmo, Philip C. Harris, Jeffrey D. Krupa, Dylan S. Rankin, Manuel Blanco Valentin, Josiah D. Hester, Yingyi Luo, John Mamish, Seda Ogrenci Memik, Thea Aarrestad, Hamza Javed, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Sioni Summers, Javier M. Duarte, Scott Hauck, Shih-Chieh Hsu, Jennifer Ngadiuba, Mia Liu, Duc Hoang, Edward Kreinar, Zhenbin Wu

CoRR, 2021

2020

Machine Learning Anomaly Detection Applications to Compact Muon Solenoid Data Quality Monitoring. (Apprentissage pour la détection d'anomalies, avec application au contrôle de qualité d'acquisition de données de l'expérience Compact Muon Solenoid).

Adrian Alan Pol

PhD thesis, 2020

2019

Detector Monitoring with Artificial Neural Networks at the CMS Experiment at the CERN Large Hadron Collider.

Adrian Alan Pol, Gianluca Cerminara, Cécile Germain, Maurizio Pierini, Agrima Seth

Comput. Softw. Big Sci., December, 2019

Anomaly Detection with Conditional Variational Autoencoders.

Adrian Alan Pol, Victor Berger, Cécile Germain, Gianluca Cerminara, Maurizio Pierini

Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

2017

Deep learning for inferring cause of data anomalies.

V. Azzolini, Maxim Borisyak, Gianluca Cerminara, Denis Derkach, Giovanni Franzoni, F. De Guio, O. Koval, Maurizio Pierini, Adrian Alan Pol, Fedor Ratnikov, Filip Siroky, Andrey Ustyuzhanin, Jean-Roch Vlimant

CoRR, 2017