Zobrazeno 1 - 10
of 3 488
pro vyhledávání: '"Masquelier, A."'
Spiking Neural Networks (SNNs) have attracted considerable attention due to their biologically inspired, event-driven nature, making them highly suitable for neuromorphic hardware. Time-to-First-Spike (TTFS) coding, where neurons fire only once durin
Externí odkaz:
http://arxiv.org/abs/2410.23619
Dilated Convolution with Learnable Spacing (DCLS) is a recent advanced convolution method that allows enlarging the receptive fields (RF) without increasing the number of parameters, like the dilated convolution, yet without imposing a regular grid.
Externí odkaz:
http://arxiv.org/abs/2408.03164
The growing interest in brain-inspired computational models arises from the remarkable problem-solving efficiency of the human brain. Action recognition, a complex task in computational neuroscience, has received significant attention due to both its
Externí odkaz:
http://arxiv.org/abs/2406.11778
Autor:
Fang, Wei, Chen, Yanqi, Ding, Jianhao, Yu, Zhaofei, Masquelier, Timothée, Chen, Ding, Huang, Liwei, Zhou, Huihui, Li, Guoqi, Tian, Yonghong
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing intere
Externí odkaz:
http://arxiv.org/abs/2310.16620
Dilated convolution with learnable spacings (DCLS) is a recent convolution method in which the positions of the kernel elements are learned throughout training by backpropagation. Its interest has recently been demonstrated in computer vision (ImageN
Externí odkaz:
http://arxiv.org/abs/2309.13972
Publikováno v:
Population Health Metrics, Vol 22, Iss 1, Pp 1-20 (2024)
Abstract Background In low- and middle-income countries with limited death registration statistics, adult mortality rates are commonly estimated through sibling survival histories (SSH). In full SSH, respondents are asked about either the age, or the
Externí odkaz:
https://doaj.org/article/0d6ba3c2a4804772b91a08c038554187
Publikováno v:
ICLR 2024
Spiking Neural Networks (SNNs) are a promising research direction for building power-efficient information processing systems, especially for temporal tasks such as speech recognition. In SNNs, delays refer to the time needed for one spike to travel
Externí odkaz:
http://arxiv.org/abs/2306.17670
In computer vision, convolutional neural networks (CNN) such as ConvNeXt, have been able to surpass state-of-the-art transformers, partly thanks to depthwise separable convolutions (DSC). DSC, as an approximation of the regular convolution, has made
Externí odkaz:
http://arxiv.org/abs/2306.00830
Dilated Convolution with Learnable Spacings (DCLS) is a recently proposed variation of the dilated convolution in which the spacings between the non-zero elements in the kernel, or equivalently their positions, are learnable. Non-integer positions ar
Externí odkaz:
http://arxiv.org/abs/2306.00817
Autor:
Folastre, Nicolas, Cao, Junhao, Oney, Gozde, Park, Sunkyu, Jamali, Arash, Masquelier, Christian, Croguennec, Laurence, Veron, Muriel, Rauch, Edgar F., Demortière, Arnaud
The technique known as 4D-STEM has recently emerged as a powerful tool for the local characterization of crystalline structures in materials, such as cathode materials for Li-ion batteries or perovskite materials for photovoltaics. However, the use o
Externí odkaz:
http://arxiv.org/abs/2305.02124