Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Li Zhangheng"'
Publikováno v:
Nanotechnology Reviews, Vol 11, Iss 1, Pp 2857-2874 (2022)
Ion erosion and carbonization in concrete are the key factors leading to the deterioration of durability. Layered double hydroxides (LDHs) are a kind of functional material with layered structures and ion exchange properties, which can capture a vari
Externí odkaz:
https://doaj.org/article/26a72f3ad6814d43a44759959a8ab059
Autor:
Li, Zhangheng, You, Keen, Zhang, Haotian, Feng, Di, Agrawal, Harsh, Li, Xiujun, Moorthy, Mohana Prasad Sathya, Nichols, Jeff, Yang, Yinfei, Gan, Zhe
Building a generalist model for user interface (UI) understanding is challenging due to various foundational issues, such as platform diversity, resolution variation, and data limitation. In this paper, we introduce Ferret-UI 2, a multimodal large la
Externí odkaz:
http://arxiv.org/abs/2410.18967
Autor:
Hong, Junyuan, Duan, Jinhao, Zhang, Chenhui, Li, Zhangheng, Xie, Chulin, Lieberman, Kelsey, Diffenderfer, James, Bartoldson, Brian, Jaiswal, Ajay, Xu, Kaidi, Kailkhura, Bhavya, Hendrycks, Dan, Song, Dawn, Wang, Zhangyang, Li, Bo
Compressing high-capability Large Language Models (LLMs) has emerged as a favored strategy for resource-efficient inferences. While state-of-the-art (SoTA) compression methods boast impressive advancements in preserving benign task performance, the p
Externí odkaz:
http://arxiv.org/abs/2403.15447
While diffusion models have recently demonstrated remarkable progress in generating realistic images, privacy risks also arise: published models or APIs could generate training images and thus leak privacy-sensitive training information. In this pape
Externí odkaz:
http://arxiv.org/abs/2403.09450
Large Language Models (LLMs) have emerged as dominant tools for various tasks, particularly when tailored for a specific target by prompt tuning. Nevertheless, concerns surrounding data privacy present obstacles due to the tuned prompts' dependency o
Externí odkaz:
http://arxiv.org/abs/2312.03724
With the rapid development of deep learning, the sizes of neural networks become larger and larger so that the training and inference often overwhelm the hardware resources. Given the fact that neural networks are often over-parameterized, one effect
Externí odkaz:
http://arxiv.org/abs/2206.07311
Publikováno v:
In Construction and Building Materials 23 February 2024 417
Publikováno v:
In Construction and Building Materials 12 January 2024 411
Publikováno v:
In Journal of Building Engineering 1 November 2023 78
In recent years, memory-augmented neural networks(MANNs) have shown promising power to enhance the memory ability of neural networks for sequential processing tasks. However, previous MANNs suffer from complex memory addressing mechanism, making them
Externí odkaz:
http://arxiv.org/abs/1906.12087