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pro vyhledávání: '"Tirilly, Pierre"'
Spike Timing-Dependent Plasticity (STDP) is a promising substitute to backpropagation for local training of Spiking Neural Networks (SNNs) on neuromorphic hardware. STDP allows SNNs to address classification tasks by combining unsupervised STDP for f
Externí odkaz:
http://arxiv.org/abs/2410.17066
Video analysis is a major computer vision task that has received a lot of attention in recent years. The current state-of-the-art performance for video analysis is achieved with Deep Neural Networks (DNNs) that have high computational costs and need
Externí odkaz:
http://arxiv.org/abs/2309.12761
Publikováno v:
Front. Neurosci. 18 (2024)
Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware has the potential to significantly reduce the energy consumption of artificial neural network training. SNNs trained with Spike Timing-Dependent Plasticity (STDP) benefit from
Externí odkaz:
http://arxiv.org/abs/2308.02194
Video analysis is a computer vision task that is useful for many applications like surveillance, human-machine interaction, and autonomous vehicles. Deep Convolutional Neural Networks (CNNs) are currently the state-of-the-art methods for video analys
Externí odkaz:
http://arxiv.org/abs/2306.13783
Current advances in technology have highlighted the importance of video analysis in the domain of computer vision. However, video analysis has considerably high computational costs with traditional artificial neural networks (ANNs). Spiking neural ne
Externí odkaz:
http://arxiv.org/abs/2205.13474
Autor:
Zirem, Yanis, Ledoux, Léa, Roussel, Lucas, Maurage, Claude Alain, Tirilly, Pierre, Le Rhun, Émilie, Meresse, Bertrand, Yagnik, Gargey, Lim, Mark J., Rothschild, Kenneth J., Duhamel, Marie, Salzet, Michel, Fournier, Isabelle
Publikováno v:
In Cell Reports Medicine 16 April 2024 5(4)
In recent years, spiking neural networks (SNNs) emerge as an alternative to deep neural networks (DNNs). SNNs present a higher computational efficiency using low-power neuromorphic hardware and require less labeled data for training using local and u
Externí odkaz:
http://arxiv.org/abs/2002.10177
Autor:
Belmonte, Romain, Allaert, Benjamin, Tirilly, Pierre, Bilasco, Ioan Marius, Djeraba, Chaabane, Sebe, Nicu
Although facial landmark localization (FLL) approaches are becoming increasingly accurate for characterizing facial regions, one question remains unanswered: what is the impact of these approaches on subsequent related tasks? In this paper, the focus
Externí odkaz:
http://arxiv.org/abs/1905.10784
Spiking neural networks (SNNs) are good candidates to produce ultra-energy-efficient hardware. However, the performance of these models is currently behind traditional methods. Introducing multi-layered SNNs is a promising way to reduce this gap. We
Externí odkaz:
http://arxiv.org/abs/1904.01908
Spiking neural networks (SNNs) equipped with latency coding and spike-timing dependent plasticity rules offer an alternative to solve the data and energy bottlenecks of standard computer vision approaches: they can learn visual features without super
Externí odkaz:
http://arxiv.org/abs/1901.04392