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A Topology Layer for Machine Learning
Date: August 12th 3:00pm at Bldg 53 Rm 1350 (Trinity)
Speaker: Brad Nelson (Stanford/SLAC)
Topology applied to real world data using persistent homology has started to find applications within machine learning, including deep learning. We present a differentiable topology layer that computes persistent homology based on level set filtrations and distance-based filtrations. We present three novel applications: the topological layer can (i) serve as a regularizer directly on data or the weights of machine learning models, (ii) construct a loss on the output of a deep generative network to incorporate topological priors, and (iii) perform topological adversarial attacks on deep networks trained with persistence features. The code is publicly available and we hope its availability will facilitate the use of persistent homology in deep learning and other gradient based applications
NASA Ames Data Sciences Group Overview
Date: August 15th 3:00pm at Bldg 53 Rm 4002 (Toluca)
Speaker: Dr. Nikunj Oza (NASA Ames Research Center)
The Data Sciences Group (DSG) at NASA Ames Research Center performs research and development of machine learning and data mining methods for application to problems of interest to NASA, including Earth Science, Aeronautics, Space Science, and Human Space Exploration, as well as related problems involving work of interest to and funded by other organizations. This talk will give an overview of the Data Sciences Group’s research and applications.
Machine learning applications of quantum annealing in high energy physics
Date: August 22nd 3:00pm at Bldg 53 Rm 4002 (Toluca)
Speaker: Alexander Zlokapa (Cal.Tech.Caltech)
Due to their limitations, noisy intermediate-scale quantum (NISQ) devices often pose challenges in encoding real-world problems and in achieving sufficiently high fidelity computations. We present methodologies and results for overcoming these challenges on the D-Wave 2X quantum annealer for two problems in high energy physics: Higgs boson classification and charged particle tracking. Each problem is solved with a different construction, offering distinct perspectives on applications of quantum annealing. The quantum annealing for machine learning (QAML) algorithm ensembles weak classifiers to create a strong classifier from the excited states in the vicinity of the ground state, taking advantage of the noise that characterizes NISQ devices to help achieve comparable results to state-of-the-art classical machine learning methods in the Higgs signal-versus-background classification problem. Under a Hopfield network formulation, we also find successful results for charged particle tracking on simulated Large Hadron Collider data. Novel classical methods are proposed to overcome the limited size and connectivity of the D-Wave architecture, enabling the analysis of events with pileup at the scale of the Large Hadron Collider during its discovery of the Higgs boson. Furthermore, the time complexity of these classical pre-processing procedures is found to scale better with track density than current state-of-the-art tracking techniques, leaving open the possibility of a quantum speedup for tracking in the future.
Pushing the Limits of Fluorescence Microscopy with adaptive imaging and machine learning
Date: September 5th 3:00pm at Bldg 53 Rm 4002 (Toluca)
Speaker: Dr. Loic A. Royer (Chan Zuckerberg Biohub)
Fluorescence microscopy lets biologist see and understand the intricate machinery at the heart of living systems and has led to numerous discoveries. Any technological progress towards improving image quality would extend the range of possible observations and would consequently open up the path to new findings. I will show how modern machine learning and smart robotic microscopes can push the boundaries of observability. One fundamental obstacle in microscopy takes the form of a trade-of between imaging speed, spatial resolution, light exposure, and imaging depth. We have shown that deep learning can circumvent these physical limitations: microscopy images can be restored even if 60-fold fewer photons are used during acquisition, isotropic resolution can be achieved even with a 10-fold under-sampling along the axial direction, and diffraction-limited structures can be resolved at 20-times higher frame-rates compared to state-of-the-art methods. Moreover, I will demonstrate how smart microscopy techniques can achieve the full optical resolution of light-sheet microscopes — instruments capable of capturing the entire developmental arch of an embryo from a single cell to a fully formed motile organism. Our instrument improves spatial resolution and signal strength two to five-fold, recovers cellular and sub-cellular structures in many regions otherwise not resolved, adapts to spatiotemporal dynamics of genetically encoded fluorescent markers and robustly optimises imaging performance during large-scale morphogenetic changes in living organisms.
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