Zobrazeno 1 - 10
of 49 395
pro vyhledávání: '"YANG XIAO"'
The unique electron deficiency of boron makes it challenging to determine the stable structures, leading to a wide variety of forms. In this work, we introduce a statistical model based on grand canonical ensemble theory that incorporates the octet r
Externí odkaz:
http://arxiv.org/abs/2412.18172
Anomaly detection aims to identify deviations from normal patterns within data. This task is particularly crucial in dynamic graphs, which are common in applications like social networks and cybersecurity, due to their evolving structures and complex
Externí odkaz:
http://arxiv.org/abs/2412.16447
Autor:
Shao, Jie-Jing, Yang, Xiao-Wen, Zhang, Bo-Wen, Chen, Baizhi, Wei, Wen-Da, Cai, Guohao, Dong, Zhenhua, Guo, Lan-Zhe, Li, Yu-feng
Recent advances in LLMs, particularly in language reasoning and tool integration, have rapidly sparked the real-world development of Language Agents. Among these, travel planning represents a prominent domain, combining academic challenges with pract
Externí odkaz:
http://arxiv.org/abs/2412.13682
Low-temperature expansion of Ising model has long been a topic of significant interest in condensed matter and statistical physics. In this paper we present new results of the coefficients in the low-temperature series of the Ising partition function
Externí odkaz:
http://arxiv.org/abs/2412.07328
Data-driven decision-making processes increasingly utilize end-to-end learnable deep neural networks to render final decisions. Sometimes, the output of the forward functions in certain layers is determined by the solutions to mathematical optimizati
Externí odkaz:
http://arxiv.org/abs/2411.19285
Recent learning-to-imitation methods have shown promising results in planning via imitating within the observation-action space. However, their ability in open environments remains constrained, particularly in long-horizon tasks. In contrast, traditi
Externí odkaz:
http://arxiv.org/abs/2411.18201
Weight initialization significantly impacts the convergence and performance of neural networks. While traditional methods like Xavier and Kaiming initialization are widely used, they often fall short for spiking neural networks (SNNs), which have dis
Externí odkaz:
http://arxiv.org/abs/2411.18250
Autor:
Luo, Yongdong, Zheng, Xiawu, Yang, Xiao, Li, Guilin, Lin, Haojia, Huang, Jinfa, Ji, Jiayi, Chao, Fei, Luo, Jiebo, Ji, Rongrong
Existing large video-language models (LVLMs) struggle to comprehend long videos correctly due to limited context. To address this problem, fine-tuning long-context LVLMs and employing GPT-based agents have emerged as promising solutions. However, fin
Externí odkaz:
http://arxiv.org/abs/2411.13093
Autor:
Yang, Xiao-Song
One fundamental problem in studying dynamical process is whether it is possible and how to construct prediction model for an unknown system via sampled time series, especially in the modern big data era. The research in this area is beneficial to exp
Externí odkaz:
http://arxiv.org/abs/2411.09230
Autor:
Wang, Chuanchuan, Mohmamed, Ahmad Sufril Azlan, Noor, Mohd Halim Bin Mohd, Yang, Xiao, Yi, Feifan, Li, Xiang
This paper presents the ARN-LSTM architecture, a novel multi-stream action recognition model designed to address the challenge of simultaneously capturing spatial motion and temporal dynamics in action sequences. Traditional methods often focus solel
Externí odkaz:
http://arxiv.org/abs/2411.01769