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Extracting Adherence Information from Electronic Health Records.

, , , , , , , and . COLING, page 680-695. International Committee on Computational Linguistics, (2020)

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Quantum Machine Learning Applications in High-Energy Physics., and . ICCAD, page 120:1-120:5. ACM, (2022)Predicting the Focus of Negation: Model and Error Analysis., , , and . ACL, page 8389-8401. Association for Computational Linguistics, (2020)Community detection with spiking neural networks for neuromorphic hardware., , and . NCS, page 9:1-9:8. ACM, (2017)Mode connectivity in the QCBM loss landscape: ICCAD Special Session Paper., , , , and . ICCAD, page 1-9. IEEE, (2021)Characterizing Quantum Classifier Utility in Natural Language Processing Workflows., , , , , , , and . QCE, page 369-370. IEEE, (2023)Building scalable variational circuit training for machine learning tasks., , , , , and . DAC, page 1351. IEEE, (2021)Error-mitigated data-driven circuit learning on noisy quantum hardware., and . Quantum Mach. Intell., 2 (1): 1-15 (2020)Sparse Hardware Embedding of Spiking Neuron Systems for Community Detection., , and . JETC, 14 (4): 40:1-40:13 (2018)Scalable quantum processor noise characterization., , , , , and . QCE, page 430-440. IEEE, (2020)Noise Robustness of Data Re-Uploading Quantum Classifiers., and . QCE, page 395-396. IEEE, (2023)