General information: Usually meetings take place on Tuesdays at 2pm in the Sycamore Conference Room (Building 40, Room 195) at SLAC, but please check the schedule. To join the mailing list either email Daniel Ratner (dratner at slac) or directly join the AI-AT-SLAC list-serv at listserv.slac.stanford.edu. Please contact Daniel Ratner if you are interested to give a talk!
Upcoming Seminar
Date: Jan 17, 2017Feb. 7, 2pm
Speaker: Austin Sendek
Title: Tractable quantum leaps in battery materials and performance via machine learning
David Schneider
Location: Sycamore Conference Room , B40-R195. (Note change of building!!!)
Time: Tuesday January 17th, 2pm
Abstract: The realization of an all solid-state lithium-ion battery would be a tremendous development towards remedying the safety issues currently plaguing lithium-ion technology. However, identifying new solid materials that will perform well as battery electrolytes is a difficult task, and our scientific intuition on whether a material is a promising candidate is often poor. Compounding on this problem is the fact that experimental measurements of performance are often very time- and cost intensive, resulting in slow progress in the field over the last several decades. We seek to accelerate discovery and design efforts by leveraging previously reported data to train learning algorithms to discriminate between high- and poor performance materials. The resulting model provides new insight into the physics of ion conduction in solids and evaluates promise in candidate materials nearly one million times faster than state-of-the-art methods. We have coupled this new model with several other heuristics to perform the first comprehensive screening of all 12,000+ known lithium-containing solids, allowing us to identify several new promising candidates.
(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: TBD
Speaker: Felix Heide
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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
Date: Jan. 17, 2017
Speaker: Austin Sendek
Title: Tractable quantum leaps in battery materials and performance via machine learning
Location: Sycamore Conference Room, B40-R195. (Note change of building!!!)
Abstract: The realization of an all solid-state lithium-ion battery would be a tremendous development towards remedying the safety issues currently plaguing lithium-ion technology. However, identifying new solid materials that will perform well as battery electrolytes is a difficult task, and our scientific intuition on whether a material is a promising candidate is often poor. Compounding on this problem is the fact that experimental measurements of performance are often very time- and cost intensive, resulting in slow progress in the field over the last several decades. We seek to accelerate discovery and design efforts by leveraging previously reported data to train learning algorithms to discriminate between high- and poor performance materials. The resulting model provides new insight into the physics of ion conduction in solids and evaluates promise in candidate materials nearly one million times faster than state-of-the-art methods. We have coupled this new model with several other heuristics to perform the first comprehensive screening of all 12,000+ known lithium-containing solids, allowing us to identify several new promising candidates.
Date: Dec 6, 2016
Speaker: Michael Kagan
Title: Deep Learning and Computer Vision in High Energy Physics
Time: Tuesday Dec 6th, 2pm
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