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ORCIDJavier M. Duarte
Javier Mauricio Duarte
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- affiliation: University of California San Diego, La Jolla, CA, USA
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Journal Articles
- 2025
[j25]Ho Fung Tsoi
, Dylan S. Rankin, Cecile Caillol, Miles D. Cranmer, Sridhara Dasu, Javier M. Duarte, Philip C. Harris, Elliot Lipeles, Vladimir Loncar:
SymbolFit: Automatic Parametric Modeling with Symbolic Regression. Comput. Softw. Big Sci. 9(1): 12 (2025)- 2024
- [j24]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, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller-Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Dongning Guo, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Shen Wang, Thomas K. Warburton:
Corrigendum: Applications and techniques for fast machine learning in science. Frontiers Big Data 6 (2024) - [j23]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, Sioni Summers, Andre Sznajder, Alexander D. Tapper, Thea Klæboe Årrestad:
Ultrafast jet classification at the HL-LHC. Mach. Learn. Sci. Technol. 5(3): 35017 (2024) - [j22]Olivia Weng, Gabriel Marcano, Vladimir Loncar, Alireza Khodamoradi, G. Abarajithan, Nojan Sheybani, Andres Meza, Farinaz Koushanfar, Kristof Denolf, Javier Mauricio Duarte, Ryan Kastner:
Tailor: Altering Skip Connections for Resource-Efficient Inference. ACM Trans. Reconfigurable Technol. Syst. 17(1): 11:1-11:23 (2024) - [j21]Javier Campos, Jovan Mitrevski, Nhan Tran, Zhen Dong, Amir Gholaminejad, Michael W. Mahoney, Javier M. Duarte:
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs. ACM Trans. Reconfigurable Technol. Syst. 17(3): 36:1-36:22 (2024) - 2023
- [j20]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, Mark S. Neubauer, O. Rind, Oksana Shadura, N. V. Tran, P. van Gemmeren, Gordon Watts, B. A. Weaver, Frank Würthwein:
Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access. Comput. Softw. Big Sci. 7(1): 5 (2023) - [j19]Raghav Kansal, Carlos Pareja, Zichun Hao, Javier M. Duarte:
JetNet: A Python package for accessing open datasets and benchmarking machine learning methods in high energy physics. J. Open Source Softw. 8(91): 5789 (2023) - [j18]Breno Orzari, Nadezda Chernyavskaya, Raphael Cóbe, Javier M. Duarte, Jefferson F. Coelho, Dimitrios Gunopulos, Raghav Kansal, Maurizio Pierini, Thiago Tomei, Mary Touranakou:
LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows. Mach. Learn. Sci. Technol. 4(4): 45023 (2023) - [j17]Rohan Shenoy, Javier M. Duarte, Christian Herwig, James Hirschauer, Daniel Noonan, Maurizio Pierini, Nhan Tran, Cristina Mantilla Suarez:
Differentiable Earth mover's distance for data compression at the high-luminosity LHC. Mach. Learn. Sci. Technol. 4(4): 45058 (2023) - [j16]Javier M. Duarte, Haoyang Li, Avik Roy, Ruike Zhu, Eliu A. Huerta, Daniel Diaz, Philip C. Harris, Raghav Kansal, Daniel S. Katz, Ishaan H. Kavoori, Volodymyr V. Kindratenko, Farouk Mokhtar, Mark S. Neubauer, Sang Eon Park, Melissa Quinnan, Roger Rusack, Zhizhen Zhao:
FAIR AI models in high energy physics. Mach. Learn. Sci. Technol. 4(4): 45062 (2023) - 2022
- [j15]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, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller-Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Dongning Guo, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Shen Wang, Thomas K. Warburton:
Applications and Techniques for Fast Machine Learning in Science. Frontiers Big Data 5: 787421 (2022) - [j14]Pratik Jawahar, Thea Aarrestad, Nadezda Chernyavskaya, Maurizio Pierini, Kinga Anna Wozniak, Jennifer Ngadiuba, Javier M. Duarte, Steven Tsan:
Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows. Frontiers Big Data 5: 803685 (2022) - [j13]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, Isobel Ojalvo, Savannah Thais, Matthew Trahms:
Graph Neural Networks for Charged Particle Tracking on FPGAs. Frontiers Big Data 5: 828666 (2022) - [j12]Javier M. Duarte, Mia Liu, Jennifer Ngadiuba, Elena Cuoco, Jesse Thaler:
Editorial: Efficient AI in particle physics and astrophysics. Frontiers Artif. Intell. 5 (2022) - [j11]Mary Touranakou, Nadezda Chernyavskaya, Javier M. Duarte, Dimitrios Gunopulos, Raghav Kansal, Breno Orzari, Maurizio Pierini, Thiago Tomei, Jean-Roch Vlimant:
Particle-based fast jet simulation at the LHC with variational autoencoders. Mach. Learn. Sci. Technol. 3(3): 35003 (2022) - [j10]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:
Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider. Nat. Mach. Intell. 4(2): 154-161 (2022) - [j9]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:
Author Correction: Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider. Nat. Mach. Intell. 4(4): 414 (2022) - 2021
- [j8]Gage DeZoort, Savannah Thais, Javier M. Duarte, Vesal Razavimaleki, Markus Atkinson, Isobel Ojalvo, Mark S. Neubauer, Peter Elmer:
Charged Particle Tracking via Edge-Classifying Interaction Networks. Comput. Softw. Big Sci. 5(1) (2021) - [j7]Benjamin Hawks, Javier M. Duarte, Nicholas J. Fraser, Alessandro Pappalardo, Nhan Tran, Yaman Umuroglu:
Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference. Frontiers Artif. Intell. 4: 676564 (2021) - [j6]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, Edward Kreinar, Sheila Sagear, Zhenbin Wu, Duc Hoang:
Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml. Mach. Learn. Sci. Technol. 2(1): 15001 (2021) - [j5]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, Dylan S. Rankin, Natchanon Suaysom, Matt Trahms, Nhan Tran:
GPU coprocessors as a service for deep learning inference in high energy physics. Mach. Learn. Sci. Technol. 2(3): 35005 (2021) - [j4]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, Dylan S. Rankin, Sergo Jindariani, Kevin Pedro, Nhan Tran, Mia Liu, Edward Kreinar, Zhenbin Wu, Duc Hoang:
Fast convolutional neural networks on FPGAs with hls4ml. Mach. Learn. Sci. Technol. 2(4): 45015 (2021) - [j3]Alexander Zlokapa, Abhishek Anand, Jean-Roch Vlimant, Javier M. Duarte, Joshua A. Job, Daniel A. Lidar, Maria Spiropulu:
Charged particle tracking with quantum annealing optimization. Quantum Mach. Intell. 3(2): 1-11 (2021) - 2020
- [j2]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, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Dylan S. Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Zhenbin Wu:
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics. Frontiers Big Data 3: 598927 (2020) - 2019
- [j1]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, Kevin Pedro, Brandon Perez, Maurizio Pierini, Dylan S. Rankin, Nhan Tran, Matthew Trahms, Aristeidis Tsaris, Colin Versteeg, Ted W. Way, Dustin Werran, Zhenbin Wu:
FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing. Comput. Softw. Big Sci. 3(1) (2019)
Conference and Workshop Papers
- 2025
- [c11]Olivia Weng, Marta Andronic, Danial Zuberi, Jiaqing Chen, Caleb Geniesse, George A. Constantinides, Nhan Tran, Nicholas J. Fraser, Javier Mauricio Duarte, Ryan Kastner:
Greater than the Sum of its LUTs: Scaling Up LUT-based Neural Networks with AmigoLUT. FPGA 2025: 25-35 - 2024
- [c10]Siqi Miao, Zhiyuan Lu, Mia Liu, Javier M. Duarte, Pan Li:
Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics. ICML 2024 - [c9]Tommaso Baldi, Javier Campos, Benjamin Hawks, Jennifer Ngadiuba, Nhan Tran, Daniel Diaz, Javier M. Duarte, Ryan Kastner, Andres Meza, Melissa Quinnan, Olivia Weng, Caleb Geniesse, Amir Gholami, Michael W. Mahoney, Vladimir Loncar, Philip C. Harris, Joshua Agar, Shuyu Qin:
Reliable edge machine learning hardware for scientific applications. VTS 2024: 1-5 - 2023
- [c8]Olivia Weng, Gabriel Marcano, Vladimir Loncar, Alireza Khodamoradi, Nojan Sheybani, Farinaz Koushanfar, Kristof Denolf, Javier Mauricio Duarte, Ryan Kastner:
Adapting Skip Connections for Resource-Efficient FPGA Inference. FPGA 2023: 229 - [c7]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:
Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs. FPL 2023: 294-298 - [c6]Rommie E. Amaro, Jiunn-Yeu Chen, Javier M. Duarte, Thomas E. Hutton, Christopher Irving, Martin C. Kandes, Amit Majumdar, Dmitry Y. Mishin, Mai H. Nguyen, Paul Rodríguez, Fernando Silva, Robert S. Sinkovits, Shawn M. Strande, Mahidhar Tatineni, Leon Si Tran, Nicole Wolter:
Voyager - An Innovative Computational Resource for Artificial Intelligence & Machine Learning Applications in Science and Engineering. PEARC 2023: 278-282 - 2022
- [c5]Shehzeen Hussain, Nojan Sheybani, Paarth Neekhara, Xinqiao Zhang, Javier Mauricio Duarte, Farinaz Koushanfar:
FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs. ICCAD 2022: 41:1-41:9 - 2021
- [c4]Colby R. Banbury, Vijay Janapa Reddi, Peter Torelli, Nat Jeffries, Csaba Király, Jeremy Holleman, Pietro Montino, David Kanter, Pete Warden, Danilo Pau, Urmish Thakker, Antonio Torrini, Jay Cordaro, Giuseppe Di Guglielmo, Javier M. Duarte, Honson Tran, Nhan Tran, Wenxu Niu, Xuesong Xu:
MLPerf Tiny Benchmark. NeurIPS Datasets and Benchmarks 2021 - [c3]Raghav Kansal, Javier M. Duarte, Hao Su, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, Dimitrios Gunopulos:
Particle Cloud Generation with Message Passing Generative Adversarial Networks. NeurIPS 2021: 23858-23871 - 2020
- [c2]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, Yu Lou, Ta-Wei Ho, Javier M. Duarte, Mia Liu:
FPGAs-as-a-Service Toolkit (FaaST). H2RC@SC 2020: 38-47 - 2019
- [c1]Javier M. Duarte, Song Han, Philip C. Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Dylan S. Rankin, Ryan A. Rivera, Sioni Summers, Nhan Tran, Zhenbin Wu:
Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications. FPGA 2019: 305
Data and Artifacts
- 2022
- [d1]Mary Touranakou, Nadezda Chernyavskaya, Javier M. Duarte, Dimitrios Gunopulos, Raghav Kansal, Breno Orzari, Maurizio Pierini, Thiago Tomei, Jean-Roch Vlimant:
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders: generator-level and reconstruction-level jets dataset. Zenodo, 2022
Informal and Other Publications
- 2025
- [i58]Jason Weitz, Dmitri Demler, Luke McDermott, Nhan Tran, Javier M. Duarte:
Neural Architecture Codesign for Fast Physics Applications. CoRR abs/2501.05515 (2025) - [i57]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, Kevin Pedro, Nhan Tran:
Track reconstruction as a service for collider physics. CoRR abs/2501.05520 (2025) - [i56]Farouk Mokhtar, Joosep Pata, Dolores Garcia, Eric Wulff, Mengke Zhang, Michael Kagan, Javier M. Duarte:
Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders. CoRR abs/2503.00131 (2025) - 2024
- [i55]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, Sioni Summers, Andre Sznajder, Alexander D. Tapper, Thea Klæboe Årrestad:
Sets are all you need: Ultrafast jet classification on FPGAs for HL-LHC. CoRR abs/2402.01876 (2024) - [i54]Siqi Miao, Zhiyuan Lu, Mia Liu, Javier M. Duarte, Pan Li:
Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics. CoRR abs/2402.12535 (2024) - [i53]Olivia Weng, Alexander Redding, Nhan Tran, Javier Mauricio Duarte, Ryan Kastner:
Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications. CoRR abs/2403.08980 (2024) - [i52]Tommaso Baldi, Javier Campos, Benjamin Hawks, Jennifer Ngadiuba, Nhan Tran, Daniel Diaz, Javier M. Duarte, Ryan Kastner, Andres Meza, Melissa Quinnan, Olivia Weng, Caleb Geniesse, Amir Gholami, Michael W. Mahoney, Vladimir Loncar, Philip C. Harris, Joshua Agar, Shuyu Qin:
Reliable edge machine learning hardware for scientific applications. CoRR abs/2406.19522 (2024) - [i51]Javier M. Duarte:
Novel machine learning applications at the LHC. CoRR abs/2409.20413 (2024) - [i50]Ho Fung Tsoi, Dylan S. Rankin, Cecile Caillol, Miles D. Cranmer, Sridhara Dasu, Javier M. Duarte, Philip C. Harris, Elliot Lipeles, Vladimir Loncar:
SymbolFit: Automatic Parametric Modeling with Symbolic Regression. CoRR abs/2411.09851 (2024) - [i49]Aaron Wang, Abhijith Gandrakota, Jennifer Ngadiuba, Vivekanand Sahu, Priyansh Bhatnagar, Elham E Khoda, Javier M. Duarte:
Interpreting Transformers for Jet Tagging. CoRR abs/2412.03673 (2024) - [i48]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, Pierre Baldi, Daniel Whiteson, Javier M. Duarte:
Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks. CoRR abs/2412.03819 (2024) - [i47]Subash Katel, Haoyang Li, Zihan Zhao, Raghav Kansal, Farouk Mokhtar, Javier M. Duarte:
Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture. CoRR abs/2412.05333 (2024) - 2023
- [i46]Olivia Weng, Gabriel Marcano, Vladimir Loncar, Alireza Khodamoradi, Nojan Sheybani, Farinaz Koushanfar, Kristof Denolf, Javier Mauricio Duarte, Ryan Kastner:
Tailor: Altering Skip Connections for Resource-Efficient Inference. CoRR abs/2301.07247 (2023) - [i45]Farouk Mokhtar, Joosep Pata, Javier M. Duarte, Eric Wulff, Maurizio Pierini, Jean-Roch Vlimant:
Progress towards an improved particle flow algorithm at CMS with machine learning. CoRR abs/2303.17657 (2023) - [i44]Javier Campos, Zhen Dong, Javier M. Duarte, Amir Gholami, Michael W. Mahoney, Jovan Mitrevski, Nhan Tran:
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs. CoRR abs/2304.06745 (2023) - [i43]Rohan Shenoy, Javier M. Duarte, Christian Herwig, James Hirschauer, Daniel Noonan, Maurizio Pierini, Nhan Tran, Cristina Mantilla Suarez:
Differentiable Earth Mover's Distance for Data Compression at the High-Luminosity LHC. CoRR abs/2306.04712 (2023) - [i42]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:
Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs. CoRR abs/2306.11330 (2023) - [i41]Joosep Pata, Eric Wulff, Farouk Mokhtar, David Southwick, Mengke Zhang, Maria Girone, Javier M. Duarte:
Scalable neural network models and terascale datasets for particle-flow reconstruction. CoRR abs/2309.06782 (2023) - [i40]Anni Li, Venkat Krishnamohan, Raghav Kansal, Rounak Sen, Steven Tsan, Zhaoyu Zhang, Javier M. Duarte:
Induced Generative Adversarial Particle Transformers. CoRR abs/2312.04757 (2023) - [i39]Luke McDermott, Jason Weitz, Dmitri Demler, Daniel Cummings, Nhan Tran, Javier M. Duarte:
Neural Architecture Codesign for Fast Bragg Peak Analysis. CoRR abs/2312.05978 (2023) - 2022
- [i38]Joosep Pata, Javier M. Duarte, Farouk Mokhtar, Eric Wulff, Jieun Yoo, Jean-Roch Vlimant, Maurizio Pierini, Maria Girone:
Machine Learning for Particle Flow Reconstruction at CMS. CoRR abs/2203.00330 (2022) - [i37]Mary Touranakou, Nadezda Chernyavskaya, Javier M. Duarte, Dimitrios Gunopoulos, Raghav Kansal, Breno Orzari, Maurizio Pierini, Thiago Tomei, Jean-Roch Vlimant:
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders. CoRR abs/2203.00520 (2022) - [i36]Savannah Thais, Paolo Calafiura, Grigorios Chachamis, Gage DeZoort, Javier M. Duarte, Sanmay Ganguly, Michael Kagan, Daniel Murnane, Mark S. Neubauer, Kazuhiro Terao:
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges. CoRR abs/2203.12852 (2022) - [i35]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, Michael Coughlin, Scott Hauck, Shih-Chieh Hsu, Elham E Khoda, Deming Chen, Mark S. Neubauer, Javier M. Duarte, Georgia Karagiorgi, Mia Liu:
Physics Community Needs, Tools, and Resources for Machine Learning. CoRR abs/2203.16255 (2022) - [i34]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, Scott Hauck, Javier M. Duarte:
QONNX: Representing Arbitrary-Precision Quantized Neural Networks. CoRR abs/2206.07527 (2022) - [i33]Hendrik Borras, Giuseppe Di Guglielmo, Javier M. Duarte, Nicolò Ghielmetti, Benjamin Hawks, Scott Hauck, Shih-Chieh Hsu, Ryan Kastner, Jason Liang, Andres Meza, Jules Muhizi, Tai Nguyen, Rushil Roy, Nhan Tran, Yaman Umuroglu, Olivia Weng, Aidan Yokuda, Michaela Blott:
Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark. CoRR abs/2206.11791 (2022) - [i32]Javier M. Duarte, Nhan Tran, Benjamin Hawks, Christian Herwig, Jules Muhizi, Shvetank Prakash, Vijay Janapa Reddi:
FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning. CoRR abs/2207.07958 (2022) - [i31]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, Mia Liu, Mark S. Neubauer, Scarlet Norberg, Alexx Perloff, Marcel Rieger, Claire Savard, Kazuhiro Terao, Savannah Thais, Avik Roy, Jean-Roch Vlimant, Grigorios Chachamis:
Data Science and Machine Learning in Education. CoRR abs/2207.09060 (2022) - [i30]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, Mark S. Neubauer, O. Rind, Oksana Shadura, N. V. Tran, P. van Gemmeren, Gordon Watts, B. A. Weaver, Frank Würthwein:
Snowmass 2021 Computational Frontier CompF4 Topical Group Report: Storage and Processing Resource Access. CoRR abs/2209.08868 (2022) - [i29]Shehzeen Hussain, Nojan Sheybani, Paarth Neekhara, Xinqiao Zhang, Javier Mauricio Duarte, Farinaz Koushanfar:
FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs. CoRR abs/2209.12391 (2022) - [i28]Eliu A. Huerta, Ben Blaiszik, L. Catherine Brinson, Kristofer E. Bouchard, Daniel Diaz, Caterina Doglioni, Javier M. Duarte, Murali Emani, Ian T. Foster, Geoffrey C. Fox, Philip C. Harris, Lukas Heinrich, Shantenu Jha, Daniel S. Katz, Volodymyr V. Kindratenko, Christine R. Kirkpatrick, Kati Lassila-Perini, Ravi K. Madduri, Mark S. Neubauer, Fotis E. Psomopoulos, Avik Roy, Oliver Rübel, Zhizhen Zhao, Ruike Zhu:
FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective. CoRR abs/2210.08973 (2022) - [i27]Farouk Mokhtar, Raghav Kansal, Javier M. Duarte:
Do graph neural networks learn traditional jet substructure? CoRR abs/2211.09912 (2022) - [i26]Raghav Kansal, Anni Li, Javier M. Duarte, Nadezda Chernyavskaya, Maurizio Pierini, Breno Orzari, Thiago Tomei:
On the Evaluation of Generative Models in High Energy Physics. CoRR abs/2211.10295 (2022) - [i25]Javier M. Duarte, Haoyang Li, Avik Roy, Ruike Zhu, Eliu A. Huerta, Daniel Diaz, Philip C. Harris, Raghav Kansal, Daniel S. Katz, Ishaan H. Kavoori, Volodymyr V. Kindratenko, Farouk Mokhtar, Mark S. Neubauer, Sang Eon Park, Melissa Quinnan, Roger Rusack, Zhizhen Zhao:
FAIR AI Models in High Energy Physics. CoRR abs/2212.05081 (2022) - [i24]Zichun Hao, Raghav Kansal, Javier M. Duarte, Nadezda Chernyavskaya:
Lorentz Group Equivariant Autoencoders. CoRR abs/2212.07347 (2022) - 2021
- [i23]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, Sergo Jindariani, Kevin Pedro, Nhan Tran, Mia Liu, Edward Kreinar, Zhenbin Wu, Duc Hoang:
Fast convolutional neural networks on FPGAs with hls4ml. CoRR abs/2101.05108 (2021) - [i22]Joosep Pata, Javier M. Duarte, Jean-Roch Vlimant, Maurizio Pierini, Maria Spiropulu:
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks. CoRR abs/2101.08578 (2021) - [i21]Benjamin Hawks, Javier M. Duarte, Nicholas J. Fraser, Alessandro Pappalardo, Nhan Tran, Yaman Umuroglu:
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference. CoRR abs/2102.11289 (2021) - [i20]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:
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices. CoRR abs/2103.05579 (2021) - [i19]Gage DeZoort, Savannah Thais, Isobel Ojalvo, Peter Elmer, Vesal Razavimaleki, Javier M. Duarte, Markus Atkinson, Mark S. Neubauer:
Charged particle tracking via edge-classifying interaction networks. CoRR abs/2103.16701 (2021) - [i18]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, Jennifer Ngadiuba, Daniel Noonan, Seda Ogrenci-Memik, Maurizio Pierini, Sioni Summers, Nhan Tran:
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC. CoRR abs/2105.01683 (2021) - [i17]Colby R. Banbury, Vijay Janapa Reddi, Peter Torelli, Jeremy Holleman, Nat Jeffries, Csaba Király, Pietro Montino, David Kanter, Sebastian Ahmed, Danilo Pau, Urmish Thakker, Antonio Torrini, Pete Warden, Jay Cordaro, Giuseppe Di Guglielmo, Javier M. Duarte, Stephen Gibellini, Videet Parekh, Honson Tran, Nhan Tran, Wenxu Niu, Xuesong Xu:
MLPerf Tiny Benchmark. CoRR abs/2106.07597 (2021) - [i16]Raghav Kansal, Javier M. Duarte, Hao Su, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, Dimitrios Gunopoulos:
Particle Cloud Generation with Message Passing Generative Adversarial Networks. CoRR abs/2106.11535 (2021) - [i15]Yifan Chen, Eliu A. Huerta, Javier M. Duarte, Philip C. Harris, Daniel S. Katz, Mark S. Neubauer, Daniel Diaz, Farouk Mokhtar, Raghav Kansal, Sang Eon Park, Volodymyr V. Kindratenko, Zhizhen Zhao, Roger Rusack:
A FAIR and AI-ready Higgs Boson Decay Dataset. CoRR abs/2108.02214 (2021) - [i14]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, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller-Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey D. Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric A. Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan S. Rankin, Simon Rothman, Ashish Sharma, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng:
Applications and Techniques for Fast Machine Learning in Science. CoRR abs/2110.13041 (2021) - [i13]Farouk Mokhtar, Raghav Kansal, Daniel Diaz, Javier M. Duarte, Joosep Pata, Maurizio Pierini, Jean-Roch Vlimant:
Explaining machine-learned particle-flow reconstruction. CoRR abs/2111.12840 (2021) - [i12]Steven Tsan, Raghav Kansal, Anthony Aportela, Daniel Diaz, Javier M. Duarte, Sukanya Krishna, Farouk Mokhtar, Jean-Roch Vlimant, Maurizio Pierini:
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance. CoRR abs/2111.12849 (2021) - [i11]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, Savannah Thais:
Graph Neural Networks for Charged Particle Tracking on FPGAs. CoRR abs/2112.02048 (2021) - 2020
- [i10]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, Zhenbin Wu:
Fast inference of Boosted Decision Trees in FPGAs for particle physics. CoRR abs/2002.02534 (2020) - [i9]Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Duc Hoang, Sergo Jindariani, Edward Kreinar, Mia Liu, Vladimir Loncar, Jennifer Ngadiuba, Kevin Pedro, Maurizio Pierini, Dylan S. Rankin, Sheila Sagear, Sioni Summers, Nhan Tran, Zhenbin Wu:
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML. CoRR abs/2003.06308 (2020) - [i8]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, Natchanon Suaysom, Matt Trahms, Nhan Tran:
GPU coprocessors as a service for deep learning inference in high energy physics. CoRR abs/2007.10359 (2020) - [i7]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, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Dylan S. Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Zhenbin Wu:
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics. CoRR abs/2008.03601 (2020) - [i6]Dylan Sheldon 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, Yu Lou, Ta-Wei Ho, Javier M. Duarte, Mia Liu:
FPGAs-as-a-Service Toolkit (FaaST). CoRR abs/2010.08556 (2020) - [i5]Raghav Kansal, Javier M. Duarte, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, Dimitrios Gunopoulos:
Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics. CoRR abs/2012.00173 (2020) - [i4]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, Dylan S. Rankin, Thea Aarrestad, Vladimir Loncar, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Mia Liu, Edward Kreinar, Zhenbin Wu:
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs. CoRR abs/2012.01563 (2020) - 2019
- [i3]Alexander Zlokapa, Abhishek Anand, Jean-Roch Vlimant, Javier M. Duarte, Joshua A. Job, Daniel A. Lidar, Maria Spiropulu:
Charged particle tracking with quantum annealing-inspired optimization. CoRR abs/1908.04475 (2019) - 2018
- [i2]Javier M. Duarte, Song Han, Philip C. Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Jennifer Ngadiuba, Maurizio Pierini, Ryan A. Rivera, Nhan Tran, Zhenbin Wu:
Fast inference of deep neural networks in FPGAs for particle physics. CoRR abs/1804.06913 (2018) - [i1]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, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Javier M. Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir V. Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemyslaw Karpinski, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark S. Neubauer, Harvey B. Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel N. Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Andrew Stewart, Bob Stienen, Ian Stockdale, Giles Chatham Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata:
Machine Learning in High Energy Physics Community White Paper. CoRR abs/1807.02876 (2018)
Coauthor Index
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