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Upcoming Seminar

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Date: Feb. 7, 2pm

Speaker: David Schneider

Location: Sycamore Conference Room (040-195)

Title: Locating Features in Detector Images with Machine Learning

Abstract: Often analysis at LCLS involves image processing of large area detectors. One goal is to find the presence, and location of certain features in the images.  We’ll look at several approaches to locating features using machine learning. The most straightforward is learning from training data that includes feature locations. When location labels are not in the training data, techniques like guided back propagation, relevance propagation,  or occlusion can be tried. We’ll discuss work on applying these approaches. We’ll also discuss ideas based on generative models like GAN’s (Generative Adversarial Networks) or VAE’s (Variational Auto Encoders).

Date: Feb. 14, 2pm

Speaker: Steve Esser (IBM)

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ProxImaL is a domain-specific language and compiler for image optimization problems that makes it easy to experiment with different problem formulations and algorithm choices. The language uses proximal operators as the fundamental building blocks of a variety of linear and nonlinear image formation models and cost functions, advanced image priors, and different noise models. The compiler intelligently chooses the best way to translate a problem formulation and choice of optimization algorithm into an efficient solver implementation. In applications to the image processing pipeline deconvolution in the presence of Poisson-distributed shot noise, and burst denoising, we show that a few lines of ProxImaL code can generate a highly-efficient solver that achieves state-of-the-art results. We also show applications to the nonlinear and nonconvex problem of phase retrieval.

Past Seminars

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Date: Feb. 7, 2pm

Speaker: David Schneider

Location: Sycamore Conference Room (040-195)

Title: Locating Features in Detector Images with Machine Learning

Abstract: Often analysis at LCLS involves image processing of large area detectors. One goal is to find the presence, and location of certain features in the images.  We’ll look at several approaches to locating features using machine learning. The most straightforward is learning from training data that includes feature locations. When location labels are not in the training data, techniques like guided back propagation, relevance propagation,  or occlusion can be tried. We’ll discuss work on applying these approaches. We’ll also discuss ideas based on generative models like GAN’s (Generative Adversarial Networks) or VAE’s (Variational Auto Encoders).

Date: Jan. 17, 2017
Speaker: Austin Sendek

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