AI

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

Peter Richtarik, Professor, Computer Science

Sep 16, 16:00 - 17:00

B1 L3 R3119

AI machine learning optimization algorithms LLM

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

Peter Richtarik, Professor, Computer Science

Sep 4, 12:00 - 13:00

B9 L2 R2325

AI machine learning optimization algorithms LLM

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

Peter Richtarik, Professor, Computer Science

Sep 15, 12:00 - 13:00

B9 L2 R2325

AI machine learning optimization algorithms LLM

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Aznaur Aliev

M.S. Student

AI Deep learning Computer Vision Reinforcement Learning

Mhd. Emad Alolaby

Associate Editor, King Abdullah University of Science and Technology

Network Design telecommunication 5G networks GSM Digital Communications optical fiber AI deep learning MATLAB

Mohammed A Shukri

Consultant (former), King Abdullah University of Science and Technology

Neural Networks AI Python BLE communication

Technical Consultant at KAUST (King Abdullah University of Science and Technology).