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Untitled work

(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895

Generalization and network design strategies

LeCun, Y. (1989) · Cited by 885

Neural Networks for Fingerprint Recognition

Baldi, P., & Chauvin, Y. (1993) · Neural Computation · doi:10.1162/neco.1993.5.3.402 · Cited by 171

Automatic Signature Verification Based on Accelerometry

Herbst, N. M., & Liu, C. N. (1977) · IBM Journal of Research and Development · doi:10.1147/rd.213.0245 · Cited by 108

A time delay neural network architecture for speech recognition

Lang, K. (1989) · Cited by 76

Signature verification from position, velocity and acceleration signals: a comparative study

Réjean Plamondon, B., & Parizeau, M. (2003) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1988.28218 · Cited by 54

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Lorette, G., & Réjean Plamondon, B. (1990) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789814439329_0002 · Cited by 26

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Guyon, I., Albrecht, P., LeCun, Y., et al. (1990) · Cited by 8

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570

3D Convolutional Neural Networks for Human Action Recognition

Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,313

Dimensionality Reduction by Learning an Invariant Mapping

Hadsell, R., Chopra, S., & LeCun, Y. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.100 · Cited by 5,336

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Bertinetto, L., Valmadre, J., Henriques, J. F., Vedaldi, A., & Torr, P. H. S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-48881-3_56 · Cited by 4,546

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Koch, G. E., Zemel, R. S., & Salakhutdinov, R. (2015) · Cited by 3,712

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Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575

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Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102363 · Cited by 2,836

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Chopra, S., Hadsell, R., & LeCun, Y. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.202 · Cited by 4,036

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Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456

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Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058

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Sun, Y. J., Wang, X., & Tang, X. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.244 · Cited by 2,023

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Xu, X., Liu, C., Feng, Q., Yin, H., Song, L., & Song, D. (2017) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3133956.3134018 · Cited by 704

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Ciaparrone, G., Luque Sánchez, F., Tabik, S., Troiano, L., Tagliaferri, R., & Herrera, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.023 · Cited by 665

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Weston, J., Ratle, F., Mobahi, H., & Collobert, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35289-8_34 · Cited by 584

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Wang, J., Zhou, F., Wen, S., Liu, X., & Lin, Y. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.283 · Cited by 565

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Varior, R. R., Shuai, B., Lu, J., Xu, D., & Wang, G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_9 · Cited by 551

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Masci, J., Boscaini, D., Bronstein, M. M., & Vandergheynst, P. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2015.112 · Cited by 721

Untitled work

(2017) · neural information processing systems

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