Spring Seminar Series 2024 – Dr. Wenpin Tang

Contractive Diffusion Models and Score Matching by Continuous Reinforcement Learning

Wenpin Tang, Ph.D.
Assistant Professor
Department of Industrial Engineering and Operations Research
Columbia University, New York, NY
https://www.columbia.edu/~wt2319

01/26/2024
11am – 12pm CST
Location: San Pedro 1, Yotta Room 430
506 Dolorosa St, San Antonio, TX 78204
Zoom: https://utsa.zoom.us/j/94807623288

Abstract:
In this talk, I will link two different topics. The past decade has witnessed the success of generative modeling (e.g., GANs and VAEs) in creating high quality samples in a wide variety of data modalities. The first part of this talk is concerned with the recently developed diffusion models, the key idea of which is to reverse a certain stochastic dynamics. I will first take a continuous-time perspective and examine the performance of different SDE schemes including VE (variance exploding) and VP (variance preserving). The discretization is more subtle, and our idea is to “contract” the reversed dynamics leading to possible new diffusion model designs. In the second part, I will talk about continuous reinforcement learning. Reinforcement Learning (RL) has been successfully applied to wide-ranging domains in the past decade. In recent years, a fast-growing body of research has extended the frontiers of continuous RL to designing model-free methods and algorithms. I will discuss the recently introduced “q-learning” and closely related policy optimization. Finally, I will highlight a natural application of continuous RL to fine-tune the score function in the diffusion models.

Spring Seminar Series 2024 – Dr. Yimin D. Zhang

Sparse Sensor Array Processing for High-Resolution Sensing

Yimin D. Zhang, Ph.D.
Associate Professor
Department of Electrical and Computer Engineering
Temple University, Philadelphia, PA
IEEE Fellow, SPIE Fellow, IEEE SPS Distinguished Lecturer
http://yiminzhang.com

2/2/2024
11am – 12pm CST
Location: San Pedro 1, Yotta Room 430
506 Dolorosa St, San Antonio, TX 78204

Organizers and Sponsors:
UTSA MATRIX AI Consortium; UTSA Department of ECE; IEEE Signal Processing Society (Distinguished Lecturer Program); IEEE Lone Star Section, Joint Communications and Signal Processing Chapter

Abstract:
Array signal processing plays a crucial role in various applications, including radar sensing, wireless communications, and medical imaging. It serves as a vital technology for tasks like directional beamforming, interference suppression, multiuser access, direction finding, signal localization, and computational imaging. The evolving needs of modern radar sensing and wireless communications demand higher spatial resolution and improved estimation accuracy. However, these requirements often translate into a significant hardware cost due to the large array aperture and the high number of array sensors.To address these challenges, sparse sensor array design and processing offer low-complexity solutions that enhance sensing capability and improve performance with a reduced number of array sensors. This presentation explores recent advancements in concepts, design strategies, and processing methods for sparse array-based sensing, aiming to achieve high spatial resolution and an increased number of degrees of freedom. These advancements leverage recent progress in compressive sensing, optimization, and machine learning. Various sparse array processing techniques will be discussed and array designs that reduce difference lag redundancies and exploit frequency diversity will be presented, providing insights into efficient and cost-effective solutions for array signal processing in diverse applications.