Zobrazeno 1 - 10
of 1 026
pro vyhledávání: '"Gil, Ángel"'
Autor:
Kudithipudi, Dhireesha, Daram, Anurag, Zyarah, Abdullah M., Zohora, Fatima Tuz, Aimone, James B., Yanguas-Gil, Angel, Soures, Nicholas, Neftci, Emre, Mattina, Matthew, Lomonaco, Vincenzo, Thiem, Clare D., Epstein, Benjamin
Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The development of lifelong learning algorithms could lead to a range of
Externí odkaz:
http://arxiv.org/abs/2310.04467
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical to designing intelligent systems. Many approaches to continual learning rely on stochastic gradient descent and its variants that employ global
Externí odkaz:
http://arxiv.org/abs/2308.04539
Autor:
Yanguas-Gil, Angel, Madireddy, Sandeep
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:
http://arxiv.org/abs/2302.13210
Autor:
Baker, Megan M., New, Alexander, Aguilar-Simon, Mario, Al-Halah, Ziad, Arnold, Sébastien M. R., Ben-Iwhiwhu, Ese, Brna, Andrew P., Brooks, Ethan, Brown, Ryan C., Daniels, Zachary, Daram, Anurag, Delattre, Fabien, Dellana, Ryan, Eaton, Eric, Fu, Haotian, Grauman, Kristen, Hostetler, Jesse, Iqbal, Shariq, Kent, Cassandra, Ketz, Nicholas, Kolouri, Soheil, Konidaris, George, Kudithipudi, Dhireesha, Learned-Miller, Erik, Lee, Seungwon, Littman, Michael L., Madireddy, Sandeep, Mendez, Jorge A., Nguyen, Eric Q., Piatko, Christine D., Pilly, Praveen K., Raghavan, Aswin, Rahman, Abrar, Ramakrishnan, Santhosh Kumar, Ratzlaff, Neale, Soltoggio, Andrea, Stone, Peter, Sur, Indranil, Tang, Zhipeng, Tiwari, Saket, Vedder, Kyle, Wang, Felix, Xu, Zifan, Yanguas-Gil, Angel, Yedidsion, Harel, Yu, Shangqun, Vallabha, Gautam K.
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:
http://arxiv.org/abs/2301.07799
Autor:
Yanguas-Gil, Angel, Madireddy, Sandeep
We have developed a model for online continual or lifelong reinforcement learning (RL) inspired on the insect brain. Our model leverages the offline training of a feature extraction and a common general policy layer to enable the convergence of RL al
Externí odkaz:
http://arxiv.org/abs/2211.16759
Autor:
Yanguas-Gil, Angel, Elam, Jeffrey W.
In this work we explore the application of deep neural networks to the optimization of atomic layer deposition processes based on thickness values obtained at different points of an ALD reactor. We introduce a dataset designed to train neural network
Externí odkaz:
http://arxiv.org/abs/2205.08378
We have developed a simulation tool to model self-limited processes such as atomic layer deposition and atomic layer etching inside reactors of arbitrary geometry. In this work, we have applied this model to two standard types of cross-flow reactors:
Externí odkaz:
http://arxiv.org/abs/2106.07132
Autor:
Redondo, Esther, Rivero-Calle, Irene, Mascarós, Enrique, Ocaña, Daniel, Jimeno, Isabel, Gil, Ángel, Linares, Manuel, Onieva-García, María Ángeles, González-Romo, Fernando, Yuste, José, Martinón-Torres, Federico
Publikováno v:
In Archivos de Bronconeumología March 2024 60(3):161-170
Autor:
Yanguas-Gil, Angel
Neuromorphic architectures are ideally suited for the implementation of smart sensors able to react, learn, and respond to a changing environment. Our work uses the insect brain as a model to understand how heterogeneous architectures, incorporating
Externí odkaz:
http://arxiv.org/abs/2104.04121
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical for designing intelligent systems. Many existing approaches to continual learning rely on stochastic gradient descent and its variants. However
Externí odkaz:
http://arxiv.org/abs/2007.08159