אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

In this talk, I will present two chapters from my Ph.D. thesis. The core of my research focuses on methods that utilize the power of modern neural networks not only for their conventional tasks such as prediction or reconstruction, but rather use the information they “learned” (usually in the forms of their gradients) in order to optimize some end-task, draw insight from the data, or even guide a generative model. The first part of the talk is dedicated to computational imaging and shows how to apply joint optimization of the forward and inverse models to improve the end performance. We demonstrate these methods on three different tasks in the fields of Magnetic Resonance Imaging (MRI) and Multiple Input Multiple Output (MIMO) radar imaging. In the second part, we show a novel method for molecular inverse design that utilizes the power of neural networks in order to propose molecules with desired properties. We developed a guided diffusion model that uses the gradients of a pre-trained prediction model to guide a pre-trained unconditional diffusion model toward the desired properties. This method allows, in general, to transform any unconditional diffusion model into a conditional generative model.