Zobrazeno 1 - 10
of 6 890
pro vyhledávání: '"Xuerui An"'
Publikováno v:
Heliyon, Vol 10, Iss 19, Pp e39003- (2024)
In order to explore the endophytic resources of Alhagi sparsifolia Shap. and identified novel antibacterial substances. Thirty endophytic fungal strains were isolated from the stems and roots of A. sparsifolia Shap. Morphological and molecular biolog
Externí odkaz:
https://doaj.org/article/6a6a5af63dc8469f88e836e8ba15b6a6
Autor:
Qiu, Xuerui, Yao, Man, Zhang, Jieyuan, Chou, Yuhong, Qiao, Ning, Zhou, Shibo, Xu, Bo, Li, Guoqi
Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. Point clouds are sparse 3D spatial data, which suggests that SNNs should be well-suited for processing them. However, when applying SNNs to point c
Externí odkaz:
http://arxiv.org/abs/2412.07360
Autor:
Yao, Man, Qiu, Xuerui, Hu, Tianxiang, Hu, Jiakui, Chou, Yuhong, Tian, Keyu, Liao, Jianxing, Leng, Luziwei, Xu, Bo, Li, Guoqi
The ambition of brain-inspired Spiking Neural Networks (SNNs) is to become a low-power alternative to traditional Artificial Neural Networks (ANNs). This work addresses two major challenges in realizing this vision: the performance gap between SNNs a
Externí odkaz:
http://arxiv.org/abs/2411.16061
Continual learning aims to incrementally acquire new concepts in data streams while resisting forgetting previous knowledge. With the rise of powerful pre-trained models (PTMs), there is a growing interest in training incremental learning systems usi
Externí odkaz:
http://arxiv.org/abs/2411.02175
We introduce AiM, an autoregressive (AR) image generative model based on Mamba architecture. AiM employs Mamba, a novel state-space model characterized by its exceptional performance for long-sequence modeling with linear time complexity, to supplant
Externí odkaz:
http://arxiv.org/abs/2408.12245
Spiking Neural Networks (SNNs) have received widespread attention due to their unique neuronal dynamics and low-power nature. Previous research empirically shows that SNNs with Poisson coding are more robust than Artificial Neural Networks (ANNs) on
Externí odkaz:
http://arxiv.org/abs/2407.20099
Recent advances in prompt learning have allowed users to interact with artificial intelligence (AI) tools in multi-turn dialogue, enabling an interactive understanding of images. However, it is difficult and inefficient to deliver information in comp
Externí odkaz:
http://arxiv.org/abs/2407.13596
Spiking Neural Networks (SNNs) are capable of encoding and processing temporal information in a biologically plausible way. However, most existing SNN-based methods for image tasks do not fully exploit this feature. Moreover, they often overlook the
Externí odkaz:
http://arxiv.org/abs/2406.03046
Autor:
Hu, JiaKui, Yao, Man, Qiu, Xuerui, Chou, Yuhong, Cai, Yuxuan, Qiao, Ning, Tian, Yonghong, XU, Bo, Li, Guoqi
Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory requirements during training and increase inference energy cost. Current training methods cannot simultaneously solve both training and inference dilemmas. This w
Externí odkaz:
http://arxiv.org/abs/2405.16466
Recent advancements in neuroscience research have propelled the development of Spiking Neural Networks (SNNs), which not only have the potential to further advance neuroscience research but also serve as an energy-efficient alternative to Artificial
Externí odkaz:
http://arxiv.org/abs/2405.13672