Pietro Liò from the University of Cambridge will discuss geometric deep learning techniques for building a digital patient twin using graph and hypergraph representation learning. The talk will focus on integrating Computational Biology and Deep Learning, considering physiological, clinical, and molecular variables. He will also cover explainable methodologies for clinicians and protein design using diffusion models. Why it matters: This highlights the growing interest in applying advanced AI techniques like geometric deep learning and diffusion models to healthcare challenges in the region, particularly for personalized medicine.
Pascal Fua from EPFL presented an approach to implementing convolutional neural nets that output complex 3D surface meshes. The method overcomes limitations in converting implicit representations to explicit surface representations. Applications include single view reconstruction, physically-driven shape optimization, and bio-medical image segmentation. Why it matters: This research advances geometric deep learning by enabling end-to-end trainable models for 3D surface mesh generation, with potential impact on various applications in computer vision and biomedical imaging in the region.
Ahmed Elhag, a PhD student at the University of Oxford, presented a new training procedure that approximates equivariance in unconstrained machine learning models via a multitask objective. The approach adds an equivariance loss to unconstrained models, allowing them to learn approximate symmetries without the computational cost of fully equivariant methods. Formulating equivariance as a flexible learning objective allows control over the extent of symmetry enforced, matching the performance of strictly equivariant baselines at a lower cost. Why it matters: This research from a speaker at MBZUAI balances rigorous theory and practical scalability in geometric deep learning, potentially accelerating drug discovery and design.
Søren Brunak presented deep learning approaches for analyzing disease trajectories using data from 7-10 million patients in Denmark and the USA. The models predict future outcomes like mortality and specific diagnoses, such as pancreatic cancer, using 15-40 years of patient data. Disease trajectories and explainable AI can generate hypotheses for molecular-level investigations into causal aspects of disease progression. Why it matters: This research demonstrates the potential of large-scale patient data and AI to improve disease prediction and generate hypotheses for further investigation into disease mechanisms relevant to regional healthcare systems.
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.