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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)

Location: Sycamore Conference Room (040-195)

Title: Energy-efficient neuromorphic hardware and its use for deep neural networks 
Abstract:Neuromorphic computing draws inspiration from the brain's structure to create energy-efficient hardware for running neural networks.  Pursuing this vision, we created the TrueNorth chip, which embodies 1 million neurons and 256 million configurable synapses in contemporary silicon technology, and runs using under 100 milliwatts.  Spiking neurons, low-precision synapses and constrained connectivity are key design factors in achieving chip efficiency, though they stand in contrast to today's conventional neural networks that use high precision neurons and synapses and have unrestricted connectivity.  Conventional networks are trained today using deep learning, a field developed independent of neuromorphic computing, and are able to achieve human-level performance on a broad spectrum of recognition tasks.  Until recently, it was unclear whether the constraints of energy-efficient neuromorphic computing were compatible with networks created through deep learning.  Taking on this challenge, we demonstrated that relatively minor modifications to deep learning methods allows for the creation of high performing networks that can run on the TrueNorth chip.  The approach was demonstrated on 8 standard datasets encompassing vision and speech, where near state-of-the-art performance was achieved while maintaining the hardware's underlying energy-efficiency to run at > 6000 frames / sec / watt.  In this talk, I will present an overview of the TrueNorth chip, our methods to train networks for this chip and a selection of performance results.


 

Date: TBD

Speaker: Felix Heide

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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.

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