Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Angel Yanguas-Gil"'
Publikováno v:
Frontiers in Neuroscience, Vol 14 (2020)
A continual learning system requires the ability to dynamically adapt and generalize to new tasks with access to only a few samples. In the central nervous system, across species, it is observed that continual and dynamic behavior in learning is an a
Externí odkaz:
https://doaj.org/article/bd05b0e54eb64daf810e572a7fef0e21
Autor:
Alvaro Rodriguez-Prieto, Ana M. Camacho, Ana M. Aragon, Miguel A. Sebastian, Angel Yanguas-Gil
Publikováno v:
IEEE Access, Vol 6, Pp 29899-29911 (2018)
In this paper, a methodology for selecting a combination of polymers and their associated additive manufacturing (AM) routes is presented. The binomial consisting of materials performance for the intended application and the most suitable AM process
Externí odkaz:
https://doaj.org/article/955f8ad4db0443dbafb908081e3e7e41
Autor:
Angel Yanguas-Gil
Publikováno v:
APL Materials, Vol 7, Iss 9, Pp 091102-091102-9 (2019)
The ability to dynamically learn and adapt to changes in the environment is one of the hallmarks of biological systems. In this work, we identify the subset of the design space of memristive materials that is optimal for dynamic learning applications
Externí odkaz:
https://doaj.org/article/3560b60a0d0e46d682105936e7b044bf
Autor:
Elsa Alvaro, Angel Yanguas-Gil
Publikováno v:
PLoS ONE, Vol 13, Iss 1, p e0189137 (2018)
This paper describes how Atomic Layer Deposition (ALD) has evolved over time using a combination of bibliometric, social network, and text analysis. We examined the rate of knowledge production as well as changes in authors, journals, and collaborato
Externí odkaz:
https://doaj.org/article/f8c88a2a61d749e8b6552380c98a1484
Autor:
Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M.R. Arnold, Ese Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik Learned-Miller, Seungwon Lee, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha
Publikováno v:
Neural Networks. 160:274-296
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::036018c7a4ebaf09e61ba1d46f5b4c3c
https://doi.org/10.1016/j.neunet.2023.01.007
https://doi.org/10.1016/j.neunet.2023.01.007
Autor:
Dhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou, Hava Siegelmann
Publikováno v:
Nature Machine Intelligence. 4:196-210
ispartof: NATURE MACHINE INTELLIGENCE vol:4 issue:3 pages:196-210 status: published
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a55395bd1b994789c5a9fc0daeeaebb
https://doi.org/10.1038/s42256-022-00452-0
https://doi.org/10.1038/s42256-022-00452-0
Autor:
Angel Yanguas-Gil, Sandeep Madireddy
In this work we have extended AutoML inspired approaches to the exploration and optimization of neuromorphic architectures. Through the integration of a parallel asynchronous model-based search approach with a simulation framework to simulate spiking
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a81f28d83e788ed7e71791d5d085cbf
Autor:
Angel Yanguas-Gil, Jeffrey W. Elam
In this work, we explore the application of deep neural networks to the optimization of atomic layer deposition (ALD) processes. In particular, we focus on a one-shot optimization problem, where we try to predict the optimal dose time that leads to s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51bf2c25beb85b87ddd1e80b80b3e49b
http://arxiv.org/abs/2205.08378
http://arxiv.org/abs/2205.08378
Autor:
Jeffrey W. Elam, Yun Gong, Liliana Stan, Gavin B. G. Stenning, Hao Chen, Jacqueline M. Cole, Angel Yanguas-Gil
Publikováno v:
ACS Applied Energy Materials. 3:3230-3241
Dye-sensitized solar cells (DSSCs) are a strong contender for next-generation photovoltaic technology with niche applications as solar-powered windows. The performance of a DSSC is particularly sus...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::460b3b5d8443272d397aaab073d81d06
https://doi.org/10.1021/acsaem.9b02001
https://doi.org/10.1021/acsaem.9b02001
Autor:
Jon E. Bratvold, Jeffrey W. Elam, John W. Freeland, Henrik Hovde Sønsteby, Angel Yanguas-Gil, Helmer Fjellvåg, Erik Skaar, Ola Nilsen
Publikováno v:
Journal of Materials Chemistry C. 8:12662-12668
Resistors are essential parts of futuristic all-oxide electronic architectures, yet easily overlooked due to their apparent simplicity. Herein, design of thin films with specific resistance spanning six orders of magnitude by partial substitution of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c3f3101d73ec65ca754c0fe19c9899cd
https://doi.org/10.1039/d0tc03406a
https://doi.org/10.1039/d0tc03406a