A. A. Pol

发表

FIT: A Metric for Model Sensitivity

M. Pierini, Tal Kopetz, Ben Zandonati, 2022, ICLR.

Data Quality Monitoring Anomaly Detection

Adrian Alan Pol, Maurizio Pierini, Cécile Germain, 2022, Artificial Intelligence for High Energy Physics.

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

Adrian Alan Pol, Maurizio Pierini, Hao Zhuang, 2020, Nature Machine Intelligence.

Anomaly Detection with Conditional Variational Autoencoders

Gianluca Cerminara, Adrian Alan Pol, Victor Berger, 2019, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA).

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

Thong Q. Nguyen, Javier Mauricio Duarte, V. Loncar, 2021, Nat. Mach. Intell..

HL-LHC Computing Review: Common Tools and Community Software

Adrian Alan Pol, A. S. Mete, A. Bocci, 2020, 2008.13636.

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

Gianluca Cerminara, Adrian Alan Pol, Maurizio Pierini, 2018, Computing and Software for Big Science.

Improving data quality monitoring via a partnership of technologies and resources between the CMS experiment at CERN and industry

M. Pierini, J. Vlimant, G. Cerminara, 2019, EPJ Web of Conferences.

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

M. Pierini, Cécile Germain, G. Cerminara, 2018, Computing and Software for Big Science.

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

Adrian Alan Pol, Josiah D. Hester, S. Jindariani, 2021, ArXiv.

Automatic deep heterogeneous quantization of Deep Neural Networks for ultra low-area, low-latency inference on the edge at particle colliders

V. Loncar, J. Ngadiuba, M. Pierini, 2020 .

Towards Optimal Compression: Joint Pruning and Quantization

M. Pierini, Tal Kopetz, Ben Zandonati, 2023, ArXiv.

Trigger Rate Anomaly Detection with Conditional Variational Autoencoders at the CMS Experiment

Gianluca Cerminara, Adrian Alan Pol, Victor Berger, 2019, NeurIPS 2019.

Anomaly detection using Deep Autoencoders for the assessment of the quality of the data acquired by the CMS experiment

Gianluca Cerminara, Adrian Alan Pol, Maurizio Pierini, 2019, EPJ Web of Conferences.

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

Thong Q. Nguyen, Javier Mauricio Duarte, V. Loncar, 2022, Nature Machine Intelligence.

Knowledge Distillation for Anomaly Detection

Adrian Alan Pol, M. Pierini, P. Elmer, 2023, ESANN.

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

Adrian Alan Pol, V. Loncar, J. Ngadiuba, 2020, Nature Machine Intelligence.

Deep Representation Learning for Trigger Monitoring

G. Cerminara, A. Hussain, A. A. Pol, 2018 .