Fast inference of deep neural networks in FPGAs for particle physics. - Javier M. Duarte; Song Han 003; Philip C. Harris; Sergo Jindariani; Edward Kreinar; Benjamin Kreis; Jennifer Ngadiuba; Maurizio Pierini; Ryan A. Rivera; Nhan Tran; Zhenbin Wu (CoRR, 2018)

Machine Learning in High Energy Physics Community White Paper. - Kim Albertsson; Piero Altoe; Dustin Anderson; Michael Andrews; Juan Pedro Araque Espinosa; Adam Aurisano; Laurent Basara; Adrian Bevan; Wahid Bhimji; Daniele Bonacorsi; Paolo Calafiura; Mario Campanelli; et al. (CoRR, 2018)

Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications. - Javier M. Duarte; Song Han 003; Philip C. Harris; Sergo Jindariani; Edward Kreinar; Benjamin Kreis; Vladimir Loncar; Jennifer Ngadiuba; Maurizio Pierini; Dylan S. Rankin; Ryan A. Rivera; Sioni Summers; et al. (FPGA, 2019)

Charged particle tracking with quantum annealing-inspired optimization. - Alexander Zlokapa; Abhishek Anand; Jean-Roch Vlimant; Javier M. Duarte; Joshua A. Job; Daniel A. Lidar; Maria Spiropulu (CoRR, 2019)

FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing. - Javier M. Duarte; Philip C. Harris; Scott Hauck; Burt Holzman; Shih-Chieh Hsu; Sergo Jindariani; Suffian Khan; Benjamin Kreis; Brian Lee; Mia Liu; Vladimir Loncar; Jennifer Ngadiuba; et al. (Comput. Softw. Big Sci., 2019)

FPGAs-as-a-Service Toolkit (FaaST). - Dylan S. Rankin; Jeffrey D. Krupa; Philip C. Harris; Maria Acosta Flechas; Burt Holzman; Thomas Klijnsma; Kevin Pedro; Nhan Tran; Scott Hauck; Shih-Chieh Hsu; Matthew Trahms; Kelvin Lin; et al. (H2RC@SC, 2020)

Fast inference of Boosted Decision Trees in FPGAs for particle physics. - Sioni Summers; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Duc Hoang; Sergo Jindariani; Edward Kreinar; Vladimir Loncar; Jennifer Ngadiuba; Maurizio Pierini; Dylan S. Rankin; Nhan Tran; et al. (CoRR, 2020)

GPU coprocessors as a service for deep learning inference in high energy physics. - Jeffrey D. Krupa; Kelvin Lin; Maria Acosta Flechas; Jack Dinsmore; Javier M. Duarte; Philip C. Harris; Scott Hauck; Burt Holzman; Shih-Chieh Hsu; Thomas Klijnsma; Mia Liu; Kevin Pedro; et al. (CoRR, 2020)

Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics. - Raghav Kansal; Javier M. Duarte; Breno Orzari; Thiago Tomei; Maurizio Pierini; Mary Touranakou; Jean-Roch Vlimant; Dimitrios Gunopoulos (CoRR, 2020)

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs. - Aneesh Heintz; Vesal Razavimaleki; Javier M. Duarte; Gage DeZoort; Isobel Ojalvo; Savannah Thais; Markus Atkinson; Mark S. Neubauer; Lindsey Gray; Sergo Jindariani; Nhan Tran; Philip C. Harris; et al. (CoRR, 2020)

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics. - Yutaro Iiyama; Gianluca Cerminara; Abhijay Gupta; Jan Kieseler; Vladimir Loncar; Maurizio Pierini; Shah Rukh Qasim; Marcel Rieger; Sioni Summers; Gerrit Van Onsem; Kinga Anna Wozniak; Jennifer Ngadiuba; et al. (Frontiers Big Data, 2020)

MLPerf Tiny Benchmark. - Colby R. Banbury; Vijay Janapa Reddi; Peter Torelli; Nat Jeffries; Csaba Király 002; Jeremy Holleman; Pietro Montino; David Kanter; Pete Warden; Danilo Pau; Urmish Thakker; Antonio Torrini; et al. (NeurIPS Datasets and Benchmarks, 2021)

Particle Cloud Generation with Message Passing Generative Adversarial Networks. - Raghav Kansal; Javier M. Duarte; Hao Su; Breno Orzari; Thiago Tomei; Maurizio Pierini; Mary Touranakou; Jean-Roch Vlimant; Dimitrios Gunopulos (NeurIPS, 2021)

Fast convolutional neural networks on FPGAs with hls4ml. - Thea Aarrestad; Vladimir Loncar; Maurizio Pierini; Sioni Summers; Jennifer Ngadiuba; Christoffer Petersson; Hampus Linander; Yutaro Iiyama; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Dylan S. Rankin; et al. (CoRR, 2021)

MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks. - Joosep Pata; Javier M. Duarte; Jean-Roch Vlimant; Maurizio Pierini; Maria Spiropulu (CoRR, 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; et al. (CoRR, 2021)

A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC. - Giuseppe Di Guglielmo; Farah Fahim; Christian Herwig; Manuel Blanco Valentin; Javier M. Duarte; Cristian Gingu; Philip C. Harris; James Hirschauer; Martin Kwok; Vladimir Loncar; Yingyi Luo; Llovizna Miranda; et al. (CoRR, 2021)

MLPerf Tiny Benchmark. - Colby R. Banbury; Vijay Janapa Reddi; Peter Torelli; Jeremy Holleman; Nat Jeffries; Csaba Király 002; Pietro Montino; David Kanter; Sebastian Ahmed; Danilo Pau; Urmish Thakker; Antonio Torrini; et al. (CoRR, 2021)

Particle Cloud Generation with Message Passing Generative Adversarial Networks. - Raghav Kansal; Javier M. Duarte; Hao Su; Breno Orzari; Thiago Tomei; Maurizio Pierini; Mary Touranakou; Jean-Roch Vlimant; Dimitrios Gunopoulos (CoRR, 2021)

A FAIR and AI-ready Higgs Boson Decay Dataset. - Yifan Chen; Eliu A. Huerta; Javier M. Duarte; Philip C. Harris; Daniel S. Katz; Mark S. Neubauer; Daniel Diaz 003; Farouk Mokhtar; Raghav Kansal; Sang Eon Park; Volodymyr V. Kindratenko; Zhizhen Zhao 001; et al. (CoRR, 2021)

Applications and Techniques for Fast Machine Learning in Science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci Memik; et al. (CoRR, 2021)

Explaining machine-learned particle-flow reconstruction. - Farouk Mokhtar; Raghav Kansal; Daniel Diaz 003; Javier M. Duarte; Joosep Pata; Maurizio Pierini; Jean-Roch Vlimant (CoRR, 2021)

Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance. - Steven Tsan; Raghav Kansal; Anthony Aportela; Daniel Diaz 003; Javier M. Duarte; Sukanya Krishna; Farouk Mokhtar; Jean-Roch Vlimant; Maurizio Pierini (CoRR, 2021)

Graph Neural Networks for Charged Particle Tracking on FPGAs. - Abdelrahman Elabd; Vesal Razavimaleki; Shi-Yu Huang; Javier M. Duarte; Markus Atkinson; Gage DeZoort; Peter Elmer; Jin-Xuan Hu; Shih-Chieh Hsu; Bo-Cheng Lai; Mark S. Neubauer; Isobel Ojalvo; et al. (CoRR, 2021)

Charged Particle Tracking via Edge-Classifying Interaction Networks. - Gage DeZoort; Savannah Thais; Javier M. Duarte; Vesal Razavimaleki; Markus Atkinson; Isobel Ojalvo; Mark S. Neubauer; Peter Elmer (Comput. Softw. Big Sci., 2021)

Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference. - Benjamin Hawks; Javier M. Duarte; Nicholas J. Fraser; Alessandro Pappalardo; Nhan Tran; Yaman Umuroglu (Frontiers Artif. Intell., 2021)

Fast convolutional neural networks on FPGAs with hls4ml. - Thea Aarrestad; Vladimir Loncar; Nicolò Ghielmetti; Maurizio Pierini; Sioni Summers; Jennifer Ngadiuba; Christoffer Petersson; Hampus Linander; Yutaro Iiyama; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; et al. (Mach. Learn. Sci. Technol., 2021)

GPU coprocessors as a service for deep learning inference in high energy physics. - Jeffrey D. Krupa; Kelvin Lin; Maria Acosta Flechas; Jack Dinsmore; Javier M. Duarte; Philip C. Harris; Scott Hauck; Burt Holzman; Shih-Chieh Hsu; Thomas Klijnsma; Mia Liu; Kevin Pedro; et al. (Mach. Learn. Sci. Technol., 2021)

Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml. - Jennifer Ngadiuba; Vladimir Loncar; Maurizio Pierini; Sioni Summers; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Dylan S. Rankin; Sergo Jindariani; Mia Liu; Kevin Pedro; Nhan Tran; et al. (Mach. Learn. Sci. Technol., 2021)

Charged particle tracking with quantum annealing optimization. - Alexander Zlokapa; Abhishek Anand; Jean-Roch Vlimant; Javier M. Duarte; Joshua A. Job; Daniel A. Lidar; Maria Spiropulu (Quantum Mach. Intell., 2021)

FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs. - Shehzeen Hussain; Nojan Sheybani; Paarth Neekhara; Xinqiao Zhang; Javier Mauricio Duarte; Farinaz Koushanfar (ICCAD, 2022)

Machine Learning for Particle Flow Reconstruction at CMS. - Joosep Pata; Javier M. Duarte; Farouk Mokhtar; Eric Wulff; Jieun Yoo; Jean-Roch Vlimant; Maurizio Pierini; Maria Girone (CoRR, 2022)

Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders. - Mary Touranakou; Nadezda Chernyavskaya; Javier M. Duarte; Dimitrios Gunopoulos; Raghav Kansal; Breno Orzari; Maurizio Pierini; Thiago Tomei; Jean-Roch Vlimant (CoRR, 2022)

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges. - Savannah Thais; Paolo Calafiura; Grigorios Chachamis; Gage DeZoort; Javier M. Duarte; Sanmay Ganguly; Michael Kagan; Daniel Murnane; Mark S. Neubauer; Kazuhiro Terao (CoRR, 2022)

Physics Community Needs, Tools, and Resources for Machine Learning. - Philip C. Harris; Erik Katsavounidis; William Patrick McCormack; Dylan S. Rankin; Yongbin Feng; Abhijith Gandrakota; Christian Herwig; Burt Holzman; Kevin Pedro; Nhan Tran; Tingjun Yang; Jennifer Ngadiuba; et al. (CoRR, 2022)

QONNX: Representing Arbitrary-Precision Quantized Neural Networks. - Alessandro Pappalardo; Yaman Umuroglu; Michaela Blott; Jovan Mitrevski; Benjamin Hawks; Nhan Tran; Vladimir Loncar; Sioni Summers; Hendrik Borras; Jules Muhizi; Matthew Trahms; Shih-Chieh Hsu; et al. (CoRR, 2022)

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark. - Hendrik Borras; Giuseppe Di Guglielmo; Javier M. Duarte; Nicolò Ghielmetti; Benjamin Hawks; Scott Hauck; Shih-Chieh Hsu; Ryan Kastner; Jason Liang; Andres Meza 001; Jules Muhizi; Tai Nguyen 003; et al. (CoRR, 2022)

FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning. - Javier M. Duarte; Nhan Tran; Benjamin Hawks; Christian Herwig; Jules Muhizi; Shvetank Prakash; Vijay Janapa Reddi (CoRR, 2022)

Data Science and Machine Learning in Education. - Gabriele Benelli; Thomas Y. Chen; Javier M. Duarte; Matthew Feickert; Matthew J. Graham; Lindsey Gray; Dan Hackett; Philip C. Harris; Shih-Chieh Hsu; Gregor Kasieczka; Elham E Khoda; Matthias Komm; et al. (CoRR, 2022)

Snowmass 2021 Computational Frontier CompF4 Topical Group Report: Storage and Processing Resource Access. - W. Bhimij; Dale Carder; Eli Dart; Javier M. Duarte; Ian Fisk; Robert W. Gardner; Chin Guok; Bo Jayatilaka; Tom Lehman; M. Lin; Carlos Maltzahn; Shawn McKee; et al. (CoRR, 2022)

FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective. - Eliu A. Huerta; Ben Blaiszik; L. Catherine Brinson; Kristofer E. Bouchard; Daniel Diaz 003; Caterina Doglioni; Javier M. Duarte; Murali Emani; Ian T. Foster; Geoffrey C. Fox; Philip C. Harris; Lukas Heinrich; et al. (CoRR, 2022)

Do graph neural networks learn traditional jet substructure? - Farouk Mokhtar; Raghav Kansal; Javier M. Duarte (CoRR, 2022)

On the Evaluation of Generative Models in High Energy Physics. - Raghav Kansal; Anni Li; Javier M. Duarte; Nadezda Chernyavskaya; Maurizio Pierini; Breno Orzari; Thiago Tomei (CoRR, 2022)

Lorentz Group Equivariant Autoencoders. - Zichun Hao; Raghav Kansal; Javier M. Duarte; Nadezda Chernyavskaya (CoRR, 2022)

Applications and Techniques for Fast Machine Learning in Science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci Memik; et al. (Frontiers Big Data, 2022)

Graph Neural Networks for Charged Particle Tracking on FPGAs. - Abdelrahman Elabd; Vesal Razavimaleki; Shi-Yu Huang; Javier M. Duarte; Markus Atkinson; Gage DeZoort; Peter Elmer; Scott Hauck; Jin-Xuan Hu; Shih-Chieh Hsu; Bo-Cheng Lai; Mark S. Neubauer; et al. (Frontiers Big Data, 2022)

Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows. - Pratik Jawahar; Thea Aarrestad; Nadezda Chernyavskaya; Maurizio Pierini; Kinga Anna Wozniak; Jennifer Ngadiuba; Javier M. Duarte; Steven Tsan (Frontiers Big Data, 2022)

Editorial: Efficient AI in particle physics and astrophysics. - Javier M. Duarte; Mia Liu; Jennifer Ngadiuba; Elena Cuoco; Jesse Thaler (Frontiers Artif. Intell., 2022)

Particle-based fast jet simulation at the LHC with variational autoencoders. - Mary Touranakou; Nadezda Chernyavskaya; Javier M. Duarte; Dimitrios Gunopulos; Raghav Kansal; Breno Orzari; Maurizio Pierini; Thiago Tomei; Jean-Roch Vlimant (Mach. Learn. Sci. Technol., 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; et al. (Nat. Mach. Intell., 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; et al. (Nat. Mach. Intell., 2022)

Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders: generator-level and reconstruction-level jets dataset. - Mary Touranakou; Nadezda Chernyavskaya; Javier M. Duarte; Dimitrios Gunopulos; Raghav Kansal; Breno Orzari; Maurizio Pierini; Thiago Tomei; Jean-Roch Vlimant (Zenodo, 2022)

Adapting Skip Connections for Resource-Efficient FPGA Inference. - Olivia Weng; Gabriel Marcano; Vladimir Loncar; Alireza Khodamoradi; Nojan Sheybani; Farinaz Koushanfar; Kristof Denolf; Javier Mauricio Duarte; Ryan Kastner (FPGA, 2023)

Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs. - Shi-Yu Huang; Yun-Chen Yang; Yu-Ru Su; Bo-Cheng Lai; Javier M. Duarte; Scott Hauck; Shih-Chieh Hsu; Jin-Xuan Hu; Mark S. Neubauer (FPL, 2023)

Voyager - An Innovative Computational Resource for Artificial Intelligence & Machine Learning Applications in Science and Engineering. - Rommie E. Amaro; Jiunn-Yeu Chen; Javier M. Duarte; Thomas E. Hutton; Christopher Irving; Martin C. Kandes; Amit Majumdar 001; Dmitry Y. Mishin; Mai H. Nguyen; Paul Rodríguez; Fernando Silva; Robert S. Sinkovits; et al. (PEARC, 2023)

Tailor: Altering Skip Connections for Resource-Efficient Inference. - Olivia Weng; Gabriel Marcano; Vladimir Loncar; Alireza Khodamoradi; Nojan Sheybani; Farinaz Koushanfar; Kristof Denolf; Javier Mauricio Duarte; Ryan Kastner (CoRR, 2023)

Progress towards an improved particle flow algorithm at CMS with machine learning. - Farouk Mokhtar; Joosep Pata; Javier M. Duarte; Eric Wulff; Maurizio Pierini; Jean-Roch Vlimant (CoRR, 2023)

End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs. - Javier Campos; Zhen Dong 003; Javier M. Duarte; Amir Gholami; Michael W. Mahoney; Jovan Mitrevski; Nhan Tran (CoRR, 2023)

Scalable neural network models and terascale datasets for particle-flow reconstruction. - Joosep Pata; Eric Wulff; Farouk Mokhtar; David Southwick; Mengke Zhang; Maria Girone; Javier M. Duarte (CoRR, 2023)

Induced Generative Adversarial Particle Transformers. - Anni Li; Venkat Krishnamohan; Raghav Kansal; Rounak Sen; Steven Tsan; Zhaoyu Zhang; Javier M. Duarte (CoRR, 2023)

Neural Architecture Codesign for Fast Bragg Peak Analysis. - Luke McDermott; Jason Weitz; Dmitri Demler; Daniel Cummings; Nhan Tran; Javier M. Duarte (CoRR, 2023)

JetNet: A Python package for accessing open datasets and benchmarking machine learning methods in high energy physics. - Raghav Kansal; Carlos Pareja; Zichun Hao; Javier M. Duarte (J. Open Source Softw., 2023)

Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access. - Wahid Bhimji; Dale Carder; Eli Dart; Javier M. Duarte; Ian Fisk; Robert W. Gardner; Chin Guok; Bo Jayatilaka; Tom Lehman; M. Lin; Carlos Maltzahn; Shawn McKee; et al. (Comput. Softw. Big Sci., 2023)

FAIR AI models in high energy physics. - Javier M. Duarte; Haoyang Li; Avik Roy; Ruike Zhu; Eliu A. Huerta; Daniel Diaz 003; Philip C. Harris; Raghav Kansal; Daniel S. Katz; Ishaan H. Kavoori; Volodymyr V. Kindratenko; Farouk Mokhtar; et al. (Mach. Learn. Sci. Technol., 2023)

LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows. - Breno Orzari; Nadezda Chernyavskaya; Raphael Cóbe; Javier M. Duarte; Jefferson F. Coelho; Dimitrios Gunopulos; Raghav Kansal; Maurizio Pierini; Thiago Tomei; Mary Touranakou (Mach. Learn. Sci. Technol., 2023)

Differentiable Earth mover's distance for data compression at the high-luminosity LHC. - Rohan Shenoy; Javier M. Duarte; Christian Herwig; James Hirschauer; Daniel Noonan; Maurizio Pierini; Nhan Tran; Cristina Mantilla Suarez (Mach. Learn. Sci. Technol., 2023)

Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics. - Siqi Miao 001; Zhiyuan Lu; Mia Liu; Javier M. Duarte; Pan Li 005 (ICML, 2024)

Reliable edge machine learning hardware for scientific applications. - Tommaso Baldi; Javier Campos; Benjamin Hawks; Jennifer Ngadiuba; Nhan Tran; Daniel Diaz 003; Javier M. Duarte; Ryan Kastner; Andres Meza 001; Melissa Quinnan; Olivia Weng; Caleb Geniesse; et al. (VTS, 2024)

Sets are all you need: Ultrafast jet classification on FPGAs for HL-LHC. - Patrick Odagiu; Zhiqiang Que; Javier M. Duarte; Johannes Haller; Gregor Kasieczka; Artur Lobanov; Vladimir Loncar; Wayne Luk; Jennifer Ngadiuba; Maurizio Pierini; Philipp Rincke; Arpita Seksaria; et al. (CoRR, 2024)

Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications. - Olivia Weng; Alexander Redding; Nhan Tran; Javier Mauricio Duarte; Ryan Kastner (CoRR, 2024)

Novel machine learning applications at the LHC. - Javier M. Duarte (CoRR, 2024)

Interpreting Transformers for Jet Tagging. - Aaron Wang; Abhijith Gandrakota; Jennifer Ngadiuba; Vivekanand Sahu; Priyansh Bhatnagar; Elham E Khoda; Javier M. Duarte (CoRR, 2024)

Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks. - Haoyang Li; Marko Stamenkovic; Alexander Shmakov; Michael Fenton; Darius Shih-Chieh Chao; Kaitlyn Maiya White; Caden Mikkelsen; Jovan Mitic; Cristina Mantilla Suarez; Melissa Quinnan; Greg Landsberg; Harvey B. Newman; et al. (CoRR, 2024)

Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture. - Subash Katel; Haoyang Li; Zihan Zhao; Raghav Kansal; Farouk Mokhtar; Javier M. Duarte (CoRR, 2024)

Corrigendum: Applications and techniques for fast machine learning in science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci-Memik; et al. (Frontiers Big Data, 2024)

Ultrafast jet classification at the HL-LHC. - Patrick Odagiu; Zhiqiang Que; Javier M. Duarte; Johannes Haller; Gregor Kasieczka; Artur Lobanov; Vladimir Loncar; Wayne Luk; Jennifer Ngadiuba; Maurizio Pierini; Philipp Rincke; Arpita Seksaria; et al. (Mach. Learn. Sci. Technol., 2024)

Tailor: Altering Skip Connections for Resource-Efficient Inference. - Olivia Weng; Gabriel Marcano; Vladimir Loncar; Alireza Khodamoradi; G. Abarajithan; Nojan Sheybani; Andres Meza 001; Farinaz Koushanfar; Kristof Denolf; Javier Mauricio Duarte; Ryan Kastner (ACM Trans. Reconfigurable Technol. Syst., 2024)

End-to-end codesign of Hessian-aware quantized neural networks for FPGAs. - Javier Campos; Jovan Mitrevski; Nhan Tran; Zhen Dong 003; Amir Gholaminejad; Michael W. Mahoney; Javier M. Duarte (ACM Trans. Reconfigurable Technol. Syst., 2024)

Greater than the Sum of its LUTs: Scaling Up LUT-based Neural Networks with AmigoLUT. - Olivia Weng; Marta Andronic; Danial Zuberi; Jiaqing Chen; Caleb Geniesse; George A. Constantinides; Nhan Tran; Nicholas J. Fraser; Javier Mauricio Duarte; Ryan Kastner (FPGA, 2025)

Neural Architecture Codesign for Fast Physics Applications. - Jason Weitz; Dmitri Demler; Luke McDermott; Nhan Tran; Javier M. Duarte (CoRR, 2025)

Track reconstruction as a service for collider physics. - Haoran Zhao; Yuan-Tang Chou; Yao Yao; Xiangyang Ju; Yongbin Feng; William Patrick McCormack; Miles Cochran-Branson; Jan-Frederik Schulte; Miaoyuan Liu; Javier M. Duarte; Philip Harris; Shih-Chieh Hsu; et al. (CoRR, 2025)

Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders. - Farouk Mokhtar; Joosep Pata; Dolores Garcia; Eric Wulff; Mengke Zhang; Michael Kagan; Javier M. Duarte (CoRR, 2025)

SymbolFit: Automatic Parametric Modeling with Symbolic Regression. - Ho Fung Tsoi; Dylan S. Rankin; Cecile Caillol; Miles D. Cranmer; Sridhara Dasu; Javier M. Duarte; Philip C. Harris; Elliot Lipeles; Vladimir Loncar (Comput. Softw. Big Sci., 2025)