Title: On the Momentum-based Methods for Training and Designing Deep Neural Networks
Speaker: Tan Nguyen - University of California
Date/Time: Friday, Oct 09 2020 - 10:00 am (GMT + 7)
About the speaker:
Dr. Tan Nguyen is currently a postdoctoral scholar in the Department of Mathematics at the University of California, Los Angeles, working with Dr. Stanley J. Osher. Tan has obtained his Ph.D. in Machine Learning from Rice University, where he was advised by Dr. Richard G. Baraniuk. His research is focused on the intersection of Deep Learning, Probabilistic Modeling, Optimization, and ODEs/PDEs. Tan gave an invited talk in the Deep Learning Theory Workshop at NeurIPS 2018 and organized the 1st Workshop on Integration of Deep Neural Models and Differential Equations at ICLR 2020. He also had two awesome long internships with Amazon AI and NVIDIA Research, during which he worked with Dr. Anima Anandkumar. Tan is the recipient of the prestigious Computing Innovation Postdoctoral Fellowship (CIFellows) from the Computing Research Association (CRA), the NSF Graduate Research Fellowship, and the IGERT Neuroengineering Traineeship. Tan received his MSEE and BSEE from Rice in May 2018 and May 2014, respectively.
- Wang, B., Nguyen, T. M. (co-first author), Bertozzi, A. L., Baraniuk, R. G., & Osher, S. J. (2020). Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. arXiv preprint arXiv:2002.10583. (Accepted at DeepMath 2020)
- Nguyen, T. M., Baraniuk, R. G., Bertozzi, A. L., Osher, S. J., & Wang, B. (2020). MomentumRNN: Integrating Momentum into Recurrent Neural Networks. arXiv preprint arXiv:2006.06919. (Accepted at NeurIPS 2020)