Zobrazeno 1 - 10
of 5 951
pro vyhledávání: '"ZHANG, JIANGUO"'
Spiking Neural Networks (SNNs) are considered as a potential candidate for the next generation of artificial intelligence with appealing characteristics such as sparse computation and inherent temporal dynamics. By adopting architectures of Artificia
Externí odkaz:
http://arxiv.org/abs/2410.18580
Autor:
Liu, Zhiwei, Yao, Weiran, Zhang, Jianguo, Murthy, Rithesh, Yang, Liangwei, Liu, Zuxin, Lan, Tian, Zhu, Ming, Tan, Juntao, Kokane, Shirley, Hoang, Thai, Niebles, Juan Carlos, Heinecke, Shelby, Wang, Huan, Savarese, Silvio, Xiong, Caiming
We introduce the Principled Reasoning and Acting (PRAct) framework, a novel method for learning and enforcing action principles from trajectory data. Central to our approach is the use of text gradients from a reflection and optimization engine to de
Externí odkaz:
http://arxiv.org/abs/2410.18528
Autor:
Zhang, Jianguo
In this paper, we introduce the quantitative coarse Baum-Connes conjecture with coefficients (or QCBC, for short) for proper metric spaces which refines the coarse Baum-Connes conjecture. And we prove that QCBC is derived by the coarse Baum-Connes co
Externí odkaz:
http://arxiv.org/abs/2410.11929
Autor:
Zhang, Jianguo
Inspired by the quantitative $K$-theory, in this paper, we introduce the coarse Baum-Connes conjecture with filtered coefficients which generalizes the original conjecture. The are two advantages for the conjecture with filtered coefficients. Firstly
Externí odkaz:
http://arxiv.org/abs/2410.11662
Autor:
Zhong, Yan, Zhao, Ruoyu, Wang, Chao, Guo, Qinghai, Zhang, Jianguo, Lu, Zhichao, Leng, Luziwei
Spiking neural networks (SNNs) provide an energy-efficient solution by utilizing the spike-based and sparse nature of biological systems. Since the advent of Transformers, SNNs have struggled to compete with artificial networks on long sequential tas
Externí odkaz:
http://arxiv.org/abs/2410.17268
Federated Learning (FL) allows collaborative machine learning training without sharing private data. Numerous studies have shown that one significant factor affecting the performance of federated learning models is the heterogeneity of data across di
Externí odkaz:
http://arxiv.org/abs/2409.18578
Autor:
Hu, Qingqiao, Zhang, Daoan, Luo, Jiebo, Gong, Zhenyu, Wiestler, Benedikt, Zhang, Jianguo, Li, Hongwei Bran
Learning meaningful and interpretable representations from high-dimensional volumetric magnetic resonance (MR) images is essential for advancing personalized medicine. While Vision Transformers (ViTs) have shown promise in handling image data, their
Externí odkaz:
http://arxiv.org/abs/2409.07746
Autor:
Zhang, Jianguo, Lan, Tian, Zhu, Ming, Liu, Zuxin, Hoang, Thai, Kokane, Shirley, Yao, Weiran, Tan, Juntao, Prabhakar, Akshara, Chen, Haolin, Liu, Zhiwei, Feng, Yihao, Awalgaonkar, Tulika, Murthy, Rithesh, Hu, Eric, Chen, Zeyuan, Xu, Ran, Niebles, Juan Carlos, Heinecke, Shelby, Wang, Huan, Savarese, Silvio, Xiong, Caiming
Autonomous agents powered by large language models (LLMs) have attracted significant research interest. However, the open-source community faces many challenges in developing specialized models for agent tasks, driven by the scarcity of high-quality
Externí odkaz:
http://arxiv.org/abs/2409.03215
Autor:
Shen, Shuaijie, Wang, Chao, Huang, Renzhuo, Zhong, Yan, Guo, Qinghai, Lu, Zhichao, Zhang, Jianguo, Leng, Luziwei
Known as low energy consumption networks, spiking neural networks (SNNs) have gained a lot of attention within the past decades. While SNNs are increasing competitive with artificial neural networks (ANNs) for vision tasks, they are rarely used for l
Externí odkaz:
http://arxiv.org/abs/2408.14909
Object parts serve as crucial intermediate representations in various downstream tasks, but part-level representation learning still has not received as much attention as other vision tasks. Previous research has established that Vision Transformer c
Externí odkaz:
http://arxiv.org/abs/2408.08108