Zobrazeno 1 - 10
of 103
pro vyhledávání: '"fast inference"'
Publikováno v:
IEEE Access, Vol 12, Pp 106912-106923 (2024)
Denoising Diffusion Models (DDMs) have become a popular tool for generating high-quality samples from complex data distributions. These models are able to capture sophisticated patterns and structures in the data, and can generate samples that are hi
Externí odkaz:
https://doaj.org/article/4828050d99b9499691f051b18df6e917
Autor:
Verhoijsen Alex, Krupskiy Pavel
Publikováno v:
Dependence Modeling, Vol 10, Iss 1, Pp 270-289 (2022)
Gaussian factor models allow the statistician to capture multivariate dependence between variables. However, they are computationally cumbersome in high dimensions and are not able to capture multivariate skewness in the data. We propose a copula mod
Externí odkaz:
https://doaj.org/article/f513ebf90c4249bb8cae822a15ba4003
Autor:
Luong Thi Hong Lan, Tran Manh Tuan, Tran Thi Ngan, Le Hoang Son, Nguyen Long Giang, Vo Truong Nhu Ngoc, Pham Van Hai
Publikováno v:
IEEE Access, Vol 8, Pp 164899-164921 (2020)
Context and Background:Complex fuzzy theory has a strong practical implication in many real-world applications. Complex Fuzzy Inference System (CFIS) is a powerful technique to overcome the challenges of uncertain, periodic data. However, a question
Externí odkaz:
https://doaj.org/article/7b4eef921e7a4988b3ac78c67e1e49a6
Autor:
Andrea Coccaro, Francesco Armando Di Bello, Stefano Giagu, Lucrezia Rambelli, Nicola Stocchetti
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 4, p 045040 (2023)
Experimental particle physics demands a sophisticated trigger and acquisition system capable to efficiently retain the collisions of interest for further investigation. Heterogeneous computing with the employment of FPGA cards may emerge as a trendin
Externí odkaz:
https://doaj.org/article/ad57da1883a84da8ae1fb78480ea6f17
Autor:
Gopalakrishnan Srinivasan, Kaushik Roy
Publikováno v:
Frontiers in Neuroscience, Vol 15 (2021)
Spiking neural networks (SNNs), with their inherent capability to learn sparse spike-based input representations over time, offer a promising solution for enabling the next generation of intelligent autonomous systems. Nevertheless, end-to-end traini
Externí odkaz:
https://doaj.org/article/66842670e7404054a1b05c5b97c42467
Autor:
Sungmin Hwang, Jeesoo Chang, Min-Hye Oh, Kyung Kyu Min, Taejin Jang, Kyungchul Park, Junsu Yu, Jong-Ho Lee, Byung-Gook Park
Publikováno v:
Frontiers in Neuroscience, Vol 15 (2021)
Spiking neural networks (SNNs) have attracted many researchers’ interests due to its biological plausibility and event-driven characteristic. In particular, recently, many studies on high-performance SNNs comparable to the conventional analog-value
Externí odkaz:
https://doaj.org/article/7444fdcf89a74c69a90ca3263c035b2d
Autor:
Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler, Vladimir Loncar, Maurizio Pierini, Shah Rukh Qasim, Marcel Rieger, Sioni Summers, Gerrit Van Onsem, Kinga Anna Wozniak, Jennifer Ngadiuba, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Zhenbin Wu
Publikováno v:
Frontiers in Big Data, Vol 3 (2021)
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGP
Externí odkaz:
https://doaj.org/article/c0766df513ec43ff8f6284d42859ca59
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