Zobrazeno 1 - 10
of 434
pro vyhledávání: '"Meng Qingyan"'
Autor:
Meng Qingyan, Sun Guirong
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
With the continuous integration of digital technology in daily life, the management path of colleges and universities also carries out development and optimization. The triple helix theory of education management model innovation in colleges and univ
Externí odkaz:
https://doaj.org/article/a312678c53654fe98b6fa71e145eb52a
Autor:
MENG Qingyan, LIU Jun
Publikováno v:
口腔疾病防治, Vol 29, Iss 5, Pp 340-345 (2021)
Orthodontic tooth movement is a complex physiological process based on periodontal tissue remodeling. Numerous factors, such as the anatomical characteristics of oral and maxillofacial complications, occlusal interference, mechanical factors and syst
Externí odkaz:
https://doaj.org/article/e69548c1a6504a66868490c59499efd3
Brain-inspired neuromorphic computing with spiking neural networks (SNNs) is a promising energy-efficient computational approach. However, successfully training SNNs in a more biologically plausible and neuromorphic-hardware-friendly way is still cha
Externí odkaz:
http://arxiv.org/abs/2407.12516
Neuromorphic computing with spiking neural networks is promising for energy-efficient artificial intelligence (AI) applications. However, different from humans who continually learn different tasks in a lifetime, neural network models suffer from cat
Externí odkaz:
http://arxiv.org/abs/2402.11984
Spiking Neural Networks (SNNs) are promising energy-efficient models for neuromorphic computing. For training the non-differentiable SNN models, the backpropagation through time (BPTT) with surrogate gradients (SG) method has achieved high performanc
Externí odkaz:
http://arxiv.org/abs/2302.14311
Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. However, most supervised SNN training methods, such as conversion from artificial neural netwo
Externí odkaz:
http://arxiv.org/abs/2302.00232
Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with low latency. Particularly, backpropagation through time (BPTT) with su
Externí odkaz:
http://arxiv.org/abs/2210.04195
Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs due to their non-differentiability. Most existing methods either suffer from high la
Externí odkaz:
http://arxiv.org/abs/2205.00459
Publikováno v:
In Journal of Differential Equations 15 September 2024 403:1-28