Publications

 

  • Guo, W., Yantir, H. E., Fouda, M. E., Eltawil, A. M., & Salama, K. N. (2021). Toward the Optimal Design and FPGA Implementation of Spiking Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 1–15. https://doi.org/10.1109/tnnls.2021.3055421
  • Guo, W., Fouda, M. E., Eltawil, A. M., & Salama, K. N. (2021). Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems. Frontiers in Neuroscience, 15. https://doi.org/10.3389/fnins.2021.638474
  • Hu, J., Kim, C., Halasz, P., Kim, J. F., Kim, J., & Szekely, G. (2021). Artificial intelligence for performance prediction of organic solvent nanofiltration membranes. Journal of Membrane Science, 619, 118513. https://doi.org/10.1016/j.memsci.2020.118513
  • Wang, D., & Xu, J. (2021). Escaping Saddle Points of Empirical Risk Privately and Scalably via DP-Trust Region Method. In Machine Learning and Knowledge Discovery in Databases (pp. 90–106). Springer International Publishing. https://doi.org/10.1007/978-3-030-67664-3_6
  • Kovalev,D.,Shulgin,E.,Richtárik,P.,Rogozin,A.,&Gasnikov,A.,(2021) ADOM: Accelerated decentralized optimization method for time-varying networks
    38th International Conference on Machine Learning
  • Chahid, A., N’Doye, I., Majoris, J. E., Berumen, M. L., & Laleg-Kirati, T. M. (2021). Model predictive control paradigms for fish growth reference tracking in precision aquaculture. Journal of Process Control, 105, 160–168. https://doi.org/10.1016/j.jprocont.2021.07.015