Deep learning

Najmeddine Dhieb

Research Engineer, Electrical and Computer Engineering

artificial intelligence Deep learning Generative AI and LLMs

Aznaur Aliev

M.S. Student

AI Deep learning Computer Vision Reinforcement Learning

Optimization for Deep Learning

Dr. Konstantin Mischenko, Samsung AI Center, Cambridge, UK

May 7, 16:00 - 17:00

B5 L5 R5209

Deep learning mathematical optimization

Abstract The field of optimization for machine learning has undergone significant changes in recent years with deep learning models increasing in scale and fine-tuning taking a more prominent role. In this presentation, I will share a perspective on the direction of changes in the field and highlight interesting research directions. I will provide real-world examples of what practitioners want from optimization methods to train deep networks at scale. I will then present my recent work on adaptive methods, such as Adam and Adagrad, and explain how we can estimate the learning rate for these

Juncai He

Research Scientist, Applied Mathematics and Computational Sciences

numerical analysis machine learning Deep learning

I am currently a research scientist in Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) at King Abdullah University of Science and Technology (KAUST). I received a B.S. degree in Mathematics and Applied Mathematics from Sichuan University in 2014. In the Summer of 2019, I received my Ph.D. degree in Computational Mathematics under the supervision of Prof. Jinchao Xu and Prof. Jun Hu at Peking University in Beijing, China. From 2019 to 2020, I worked as a Postdoctoral Scholar supervised by Prof. Jinchao Xu in The Center for Computational Mathematics and Application