1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X., TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. 19
2. Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning;Abrol;Nat Commun,2021
3. Confound modelling in UK Biobank brain imaging;Alfaro-Almagro;Neuroimage,2021
4. Single subject prediction of brain disorders in neuroimaging: promises and pitfalls;Arbabshirani;Neuroimage,2017
5. Growth hormone, insulin-like growth factor-1 and the aging brain;Ashpole;Exp. Gerontol.,2015