Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Kulkarni, Shruti P."'
Autor:
Gobin, Derek, Snyder, Shay, Cong, Guojing, Kulkarni, Shruti R., Schuman, Catherine, Parsa, Maryam
Many of today's most interesting questions involve understanding and interpreting complex relationships within graph-based structures. For instance, in materials science, predicting material properties often relies on analyzing the intricate network
Externí odkaz:
http://arxiv.org/abs/2405.04478
Autor:
Snyder, Shay, Clerico, Victoria, Cong, Guojing, Kulkarni, Shruti, Schuman, Catherine, Risbud, Sumedh R., Parsa, Maryam
Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements utilize graph convolutional neural networks to extract features within
Externí odkaz:
http://arxiv.org/abs/2404.17048
Autor:
Dickinson, Jennet, Kovach-Fuentes, Rachel, Gray, Lindsey, Swartz, Morris, Di Guglielmo, Giuseppe, Bean, Alice, Berry, Doug, Valentin, Manuel Blanco, DiPetrillo, Karri, Fahim, Farah, Hirschauer, James, Kulkarni, Shruti R., Lipton, Ron, Maksimovic, Petar, Mills, Corrinne, Neubauer, Mark S., Parpillon, Benjamin, Pradhan, Gauri, Syal, Chinar, Tran, Nhan, Wen, Dahai, Yoo, Jieun, Young, Aaron
The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), and remains so for the HL-LHC. Next-generation pixel sensors will be sufficiently fine-grained to determine
Externí odkaz:
http://arxiv.org/abs/2312.11676
Autor:
Lu, Ziyu, Tabassum, Anika, Kulkarni, Shruti, Mi, Lu, Kutz, J. Nathan, Shea-Brown, Eric, Lim, Seung-Hwan
This paper explores the potential of the transformer models for learning Granger causality in networks with complex nonlinear dynamics at every node, as in neurobiological and biophysical networks. Our study primarily focuses on a proof-of-concept in
Externí odkaz:
http://arxiv.org/abs/2311.06928
Autor:
Yoo, Jieun, Dickinson, Jennet, Swartz, Morris, Di Guglielmo, Giuseppe, Bean, Alice, Berry, Douglas, Valentin, Manuel Blanco, DiPetrillo, Karri, Fahim, Farah, Gray, Lindsey, Hirschauer, James, Kulkarni, Shruti R., Lipton, Ron, Maksimovic, Petar, Mills, Corrinne, Neubauer, Mark S., Parpillon, Benjamin, Pradhan, Gauri, Syal, Chinar, Tran, Nhan, Wen, Dahai, Young, Aaron
Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced, leading to unprecedented data rates exceeding those foreseen at the Hi
Externí odkaz:
http://arxiv.org/abs/2310.02474
Autor:
Kulkarni, Shruti R., Young, Aaron, Date, Prasanna, Miniskar, Narasinga Rao, Vetter, Jeffrey S., Fahim, Farah, Parpillon, Benjamin, Dickinson, Jennet, Tran, Nhan, Yoo, Jieun, Mills, Corrinne, Swartz, Morris, Maksimovic, Petar, Schuman, Catherine D., Bean, Alice
This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We presen
Externí odkaz:
http://arxiv.org/abs/2307.11242
Autor:
Date, Prasanna, Gunaratne, Chathika, Kulkarni, Shruti, Patton, Robert, Coletti, Mark, Potok, Thomas
In many neuromorphic workflows, simulators play a vital role for important tasks such as training spiking neural networks (SNNs), running neuroscience simulations, and designing, implementing and testing neuromorphic algorithms. Currently available s
Externí odkaz:
http://arxiv.org/abs/2305.02510
Autor:
Date, Prasanna, Kulkarni, Shruti, Young, Aaron, Schuman, Catherine, Potok, Thomas, Vetter, Jeffrey
Neuromorphic computers perform computations by emulating the human brain, and use extremely low power. They are expected to be indispensable for energy-efficient computing in the future. While they are primarily used in spiking neural network-based m
Externí odkaz:
http://arxiv.org/abs/2208.07468
Akademický článek
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We describe a novel spiking neural network (SNN) for automated, real-time handwritten digit classification and its implementation on a GP-GPU platform. Information processing within the network, from feature extraction to classification is implemente
Externí odkaz:
http://arxiv.org/abs/1711.03637