[slides] JSALT 2025 - Plenary Talk - Ramani Duraiswami: Differentiable Modeling for Machine Learning

📍 Live from FIT, Brno University of Technology (Czech Republic), room E112
🕘 July 10th, 2025 — 11:00 CEST
🎙️ Ramani Duraiswami [University of Maryland, College Park]

Learning via deep neural networks has achieved great success in learning functions relating large datasets to their labels in areas like natural language processing, computer vision, and speech processing. It continues to revolutionize many areas of science and society. I will briefly describe various themes of machine learning research underway in my group at UMD: Differentiable Modeling, speeding up the Attention mechanism in Training and Inference in Transformer architectures, and Large Audio Language Models.

The main part of my talk will be focused on the use of differentiable forward modeling in various ways.

An under-appreciated aspect of the deep learning revolution is the use of automatic differentiation and backpropagation on differentiable computational graphs to obtain the parameters specifying the networks. Before learning from data became the method of choice, scientists spent entire careers developing forward models that captured much scientific knowledge about the domains they worked on. These models were based on mathematics, physics, biology, and other scientific areas they worked on. Making these forward models differentiable allows for this knowledge to be incorporated in deep learning architectures. This allows achieving computational pipelines that can incorporate deep learning for tasks like parameter optimization, cost function minimization, inverse problem solution, implicit neural representations, and learning explainable models, that work well in domains where data is sparse.

We apply these ideas in domains like computer graphics, human hearing, room acoustics, signal processing, and in the solution of inverse problems arising in mathematical physics. We will present example solutions and results.

Bio: Ramani Duraiswami is Professor in the department of Computer Science at the University of Maryland, College Park. He also has appointments at Artificial Intelligence Institute at Maryland, UMIACS, Electrical Engineering, Robotics program, Neural and Cognitive Sciences program, and the Applied Math and Scientific Computing Program at the same university. Prof. Duraiswami got his B. Tech. from IIT Bombay and his Ph.D. from The Johns Hopkins University. His research interests are in machine learning, scientific computing and computational perception. Two companies have been spun out based on his research. The audio engine used in content that plays on the millions of shipping VR headsets, PCs, and headphones is based on work from his lab.

Видео [slides] JSALT 2025 - Plenary Talk - Ramani Duraiswami: Differentiable Modeling for Machine Learning канала Center for Language & Speech Processing(CLSP), JHU