This article discusses a talk by Dr. David Xianfeng Gu at MBZUAI on gaining a geometric understanding of deep learning. The talk addresses questions such as what a DL system learns, how it learns, and how to improve the learning process. Dr. Gu is a professor at SUNY Stony Brook and affiliated with multiple prestigious institutions. Why it matters: Understanding the fundamentals of deep learning is crucial for advancing AI research and development in the region.
MBZUAI's Associate Professor of Machine Learning, Gus Xia, will co-teach an introductory AI course with Monojit Choudhury, emphasizing experiential learning and fundamental principles. Xia's background spans computer science, music, and metaphysics, aiming to inspire students to innovate in AI. More than 100 students will join MBZUAI's Bachelor of Science in Artificial Intelligence program. Why it matters: This interdisciplinary approach at MBZUAI could cultivate a new generation of AI researchers with diverse perspectives and innovative problem-solving skills.
KAUST Ph.D. students David Evangelista and Xianjin Yang won best paper awards at international conferences this summer for their work in mean-field game theory. Evangelista's paper focused on solutions for stationary mean-field games with congestion, while Yang's paper developed numerical methods for homogenization problems. The awards were presented at the 18th International Symposium on Dynamic Games and Applications in France and the 12th American Institute of Mathematical Sciences (AIMS) Conference in Taiwan. Why it matters: The recognition highlights KAUST's strength in applied mathematics and computational science, specifically in the emerging field of mean-field games with applications across various domains.
Dr. Zeke Xie from HKUST(GZ) presented research on noise initialization and sampling strategies for diffusion models. The talk covered golden noise for text-to-image models, zigzag diffusion sampling, smooth initializations for video diffusion, and leveraging image diffusion for video synthesis. Xie leads the xLeaF Lab, focusing on optimization, inference, and generative AI, with previous experience at Baidu Research. Why it matters: The work addresses core challenges in improving the quality and diversity of generated content from diffusion models, a key area of advancement for AI applications in the region.
Dr. Hao Dong from Peking University presented research on addressing the challenge of limited large-scale training data in embodied AI, particularly for manipulation, task planning, and navigation. The presentation covered simulation learning and large models. Dr. Dong is a chief scientist of China's National Key Research and Development Program and an area chair/associate editor for NeurIPS, CVPR, AAAI, and ICRA. Why it matters: Overcoming data scarcity is crucial for advancing embodied AI research and enabling more sophisticated robotic applications in the region.
KAUST Professor David Keyes will chair the International Supercomputing Conference (ISC) 2020 in Frankfurt, Germany. Keyes is the director of KAUST's Extreme Computing Research Center and will be the first program chair from a Middle Eastern institution. The conference will address high performance computing (HPC) topics including processing, storage, algorithms, and the convergence of simulation, machine learning, and big data. Why it matters: This highlights KAUST's leadership in HPC within the Middle East, as the university is home to Shaheen II, the region's most powerful supercomputer.
Yanwei Fu from Fudan University will present research on multimodal models, robotic grasping, and fMRI neural decoding. Topics include few-shot learning, object-centered self-supervised learning, image manipulation, and visual-language alignment. The research also covers Transformer compression and applications of large models with MVS 3D modeling in robotic arm grasping. Why it matters: While the talk is not directly about Middle East AI, the topics covered are core to advancing AI research and applications in the region.