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Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks.

, , , , and . Int. J. Comput. Assist. Radiol. Surg., 14 (9): 1611-1617 (2019)

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Deep learning for time series classification. (Apprentissage profond pour la classification des séries temporelles).. University of Upper Alsace, Mulhouse, France, (2020)Knowledge-driven Biometric Authentication in Virtual Reality., , and . CHI Extended Abstracts, page 1-10. ACM, (2020)Augmentation de données pour la classification de séries temporelles par réseaux de neurones profonds résiduels., , , , and . EGC, volume E-35 of RNTI, page 375-376. Éditions RNTI, (2019)Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks., , , , and . Int. J. Comput. Assist. Radiol. Surg., 14 (9): 1611-1617 (2019)Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark., , , , and . CoRR, (2023)Deep learning for time series classification: a review, , , , and . Data Mining and Knowledge Discovery, 33 (4): 917--963 (Jul 1, 2019)Deep learning for time series classification.. CoRR, (2020)Automatic Alignment of Surgical Videos Using Kinematic Data., , , , , and . AIME, volume 11526 of Lecture Notes in Computer Science, page 104-113. Springer, (2019)ShapeDBA: Generating Effective Time Series Prototypes Using ShapeDTW Barycenter Averaging., , , , , , , and . AALTD@ECML/PKDD, volume 14343 of Lecture Notes in Computer Science, page 127-142. Springer, (2023)Deep Neural Network Ensembles for Time Series Classification., , , , and . IJCNN, page 1-6. IEEE, (2019)