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pro vyhledávání: '"Johnson, Erik A."'
Autor:
Norman-Tenazas, Raphael, Kleinberg, David, Johnson, Erik C., Lathrop, Daniel P., Roos, Matthew J.
It has been shown that unclocked, recurrent networks of Boolean gates in FPGAs can be used for low-SWaP reservoir computing. In such systems, topology and node functionality of the network are randomly initialized. To create a network that solves a t
Externí odkaz:
http://arxiv.org/abs/2403.13105
The Geiger photodiodes, or single photon avalanche photodiodes, for a radiation tolerant solid-state photomultiplier (SSPM) are being designed using Aluminum Gallium Arsenide (AlGaAs) and are less than 1 micrometer thick. Studies on the changes in da
Externí odkaz:
http://arxiv.org/abs/2402.08838
Autor:
Wimbish, Miguel E., Guittari, Nicole K., Rose, Victoria A., Rivera Jr, Jorge L., Rivlin, Patricia K., Hinton, Mark A., Matelsky, Jordan K., Stock, Nicole E., Wester, Brock A., Johnson, Erik C., Gray-Roncal, William R.
High resolution volumetric neuroimaging datasets from electron microscopy (EM) and x-ray micro and holographic-nano tomography (XRM/XHN) are being generated at an increasing rate and by a growing number of research teams. These datasets are derived f
Externí odkaz:
http://arxiv.org/abs/2401.15251
Autor:
Johnson, Erik C., Nguyen, Thinh T., Dichter, Benjamin K., Zappulla, Frank, Kosma, Montgomery, Gunalan, Kabilar, Halchenko, Yaroslav O., Neufeld, Shay Q., Schirner, Michael, Ritter, Petra, Martone, Maryann E., Wester, Brock, Pestilli, Franco, Yatsenko, Dimitri
Scientists are adopting new approaches to scale up their activities and goals. Progress in neurotechnologies, artificial intelligence, automation, and tools for collaboration promises new bursts of discoveries. However, compared to other disciplines
Externí odkaz:
http://arxiv.org/abs/2401.00077
We propose a novel modular inference approach combining two different generative models -- generative adversarial networks (GAN) and normalizing flows -- to approximate the posterior distribution of physics-based Bayesian inverse problems framed in h
Externí odkaz:
http://arxiv.org/abs/2310.04690
Autor:
Johnson, Erik C., Robinson, Brian S., Vallabha, Gautam K., Joyce, Justin, Matelsky, Jordan K., Norman-Tenazas, Raphael, Western, Isaac, Villafañe-Delgado, Marisel, Cervantes, Martha, Robinette, Michael S., Reddy, Arun V., Kitchell, Lindsey, Rivlin, Patricia K., Reilly, Elizabeth P., Drenkow, Nathan, Roos, Matthew J., Wang, I-Jeng, Wester, Brock A., Gray-Roncal, William R., Hoffmann, Joan A.
Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We envision a pipeline to utilize large ne
Externí odkaz:
http://arxiv.org/abs/2305.17300
Autor:
Jabini, Amin, Johnson, Erik A.
Optimal sensor placement enhances the efficiency of a variety of applications for monitoring dynamical systems. It has been established that deterministic solutions to the sensor placement problem are insufficient due to the many uncertainties in sys
Externí odkaz:
http://arxiv.org/abs/2303.09750
Autor:
Quesada, Jorge, Sathidevi, Lakshmi, Liu, Ran, Ahad, Nauman, Jackson, Joy M., Azabou, Mehdi, Xiao, Jingyun, Liding, Christopher, Jin, Matthew, Urzay, Carolina, Gray-Roncal, William, Johnson, Erik C., Dyer, Eva L.
There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to buil
Externí odkaz:
http://arxiv.org/abs/2301.00345
Autor:
Robinson, Brian S., Lau, Clare W., New, Alexander, Nichols, Shane M., Johnson, Erik C., Wolmetz, Michael, Coon, William G.
Learning new tasks and skills in succession without losing prior learning (i.e., catastrophic forgetting) is a computational challenge for both artificial and biological neural networks, yet artificial systems struggle to achieve parity with their bi
Externí odkaz:
http://arxiv.org/abs/2209.05245
Autor:
Johnson, Erik C. M., Habermann, Marc, Shimada, Soshi, Golyanik, Vladislav, Theobalt, Christian
Capturing general deforming scenes from monocular RGB video is crucial for many computer graphics and vision applications. However, current approaches suffer from drawbacks such as struggling with large scene deformations, inaccurate shape completion
Externí odkaz:
http://arxiv.org/abs/2206.08368