Zobrazeno 1 - 10
of 961
pro vyhledávání: '"Huang Haiping"'
Publikováno v:
网络与信息安全学报, Vol 10, Pp 95-105 (2024)
As the demand for international travel escalates, the individual health passport has emerged as an essential instrument for verifying the health status of travelers and fulfilling entry criteria. To tackle the challenges associated with the global sh
Externí odkaz:
https://doaj.org/article/f13b99aab68744ffb8880c02eadd0961
Recent advances in structured 3D Gaussians for view-adaptive rendering, particularly through methods like Scaffold-GS, have demonstrated promising results in neural scene representation. However, existing approaches still face challenges in perceptua
Externí odkaz:
http://arxiv.org/abs/2411.05731
Autor:
Wang, Shishe, Huang, Haiping
High dimensional dynamics play a vital role in brain function, ecological systems, and neuro-inspired machine learning. Where and how these dynamics are confined in the phase space remains challenging to solve. Here, we provide an analytic argument t
Externí odkaz:
http://arxiv.org/abs/2410.19348
Autor:
Yu, Zhendong, Huang, Haiping
With an increasing amount of observations on the dynamics of many complex systems, it is required to reveal the underlying mechanisms behind these complex dynamics, which is fundamentally important in many scientific fields such as climate, financial
Externí odkaz:
http://arxiv.org/abs/2409.04240
Large language models show a surprising in-context learning ability -- being able to use a prompt to form a prediction for a query, yet without additional training, in stark contrast to old-fashioned supervised learning. Providing a mechanistic inter
Externí odkaz:
http://arxiv.org/abs/2408.02288
Autor:
Yu, Zhendong, Huang, Haiping
Generative diffusion models apply the concept of Langevin dynamics in physics to machine leaning, attracting a lot of interests from engineering, statistics and physics, but a complete picture about inherent mechanisms is still lacking. In this paper
Externí odkaz:
http://arxiv.org/abs/2405.11932
Publikováno v:
Sci. China-Phys. Mech. Astron. 68, 210511 (2025)
Distinct from human cognitive processing, deep neural networks trained by backpropagation can be easily fooled by adversarial examples. To design a semantically meaningful representation learning, we discard backpropagation, and instead, propose a lo
Externí odkaz:
http://arxiv.org/abs/2404.13631
Crafting effective topic models for brief texts, like tweets and news headlines, is essential for capturing the swift shifts in social dynamics. Traditional topic models, however, often fall short in accurately representing the semantic intricacies o
Externí odkaz:
http://arxiv.org/abs/2403.17706
Autor:
Qiu, Junbin, Huang, Haiping
Publikováno v:
2025 Commun. Theor. Phys. 77 035601
Understanding neural dynamics is a central topic in machine learning, non-linear physics and neuroscience. However, the dynamics is non-linear, stochastic and particularly non-gradient, i.e., the driving force can not be written as gradient of a pote
Externí odkaz:
http://arxiv.org/abs/2401.10009