- Neutral pion reconstruction using machine learning in the \minerva experiment at ⟨E ν ⟩∼6 GeV, arXiv:2103.06992, 2021.
- hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices, arXiv:2103.05579, 2021.
- DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains, arXiv:2103.01373, 2021.
- Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference, arXiv:2102.11289, 2021.
- Machine learning of high dimensional data on a noisy quantum processor, arXiv:2101.09581, 2021.
- Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster, arXiv:2011.07371, 2021.
- Measuring QCD Splittings with Invertible Networks, arXiv:2012.09873, 2020.
- Domain adaptation techniques for improved cross-domain study of galaxy mergers, arXiv:2011.03591, 2020.
- FPGAs-as-a-Service Toolkit (FaaST), arXiv:2010.08556, 2020.
- GPU-accelerated machine learning inference as a service for computing in neutrino experiments, arXiv:2009.04509, 2020.
- GPU coprocessors as a service for deep learning inference in high energy physics, arXiv:2007.10359, 2020.
- DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks, arXiv:1810.01483, 2020.
- DeepMerge: Classifying High-redshift Merging Galaxies with Deep Neural Networks, arXiv:2004.11981, 2020.
- A Dynamic Reduction Network for Point Clouds, arXiv:2003.08013, 2020.
- Dynamically Reconfigurable Data Readout of Pixel Detectors for Automatic Synchronization with Data Acquisition Systems, published in Sensors 20 (9), p. 2560, 2020.
- Event Generation with Normalizing Flows, arXiv:2001.10028, 2020.
- i-flow: High-dimensional Integration and Sampling with Normalizing Flows, arXiv:2001.05486, 2020.
- Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer, arXiv:1911.06259, 2020.
- Interaction networks for the identification of boosted H → bb decays, arXiv:1909.12285, 2020.
- FPGA-accelerated machine learning inference as a service for particle physics computing, arXiv:1904.08986, 2019.
- Novel deep learning methods for track reconstruction, arXiv:1810.06111, 2018.
- Fast inference of deep neural networks in FPGAs for particle physics, arXiv:1804.06913, 2018.
