Zobrazeno 1 - 10
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pro vyhledávání: '"Wu, ZiJun"'
Autor:
Zhang, Xiang, Li, Senyu, Shi, Ning, Hauer, Bradley, Wu, Zijun, Kondrak, Grzegorz, Abdul-Mageed, Muhammad, Lakshmanan, Laks V. S.
Recent developments in multimodal methodologies have marked the beginning of an exciting era for models adept at processing diverse data types, encompassing text, audio, and visual content. Models like GPT-4V, which merge computer vision with advance
Externí odkaz:
http://arxiv.org/abs/2411.09273
In the past few years, channel-wise and spatial-wise attention blocks have been widely adopted as supplementary modules in deep neural networks, enhancing network representational abilities while introducing low complexity. Most attention modules fol
Externí odkaz:
http://arxiv.org/abs/2411.08333
Recent advancements in Large Language Models (LLMs) have significantly enhanced their capacity to process long contexts. However, effectively utilizing this long context remains a challenge due to the issue of distraction, where irrelevant informatio
Externí odkaz:
http://arxiv.org/abs/2411.05928
Recent studies have demonstrated the potential to control paraphrase generation, such as through syntax, which has broad applications in various downstream tasks. However, these methods often require detailed parse trees or syntactic exemplars, count
Externí odkaz:
http://arxiv.org/abs/2405.11277
Recent advancements in multimodal techniques open exciting possibilities for models excelling in diverse tasks involving text, audio, and image processing. Models like GPT-4V, blending computer vision and language modeling, excel in complex text and
Externí odkaz:
http://arxiv.org/abs/2310.12520
Prompt tuning in natural language processing (NLP) has become an increasingly popular method for adapting large language models to specific tasks. However, the transferability of these prompts, especially continuous prompts, between different models
Externí odkaz:
http://arxiv.org/abs/2310.01691
In Natural Language Processing (NLP), predicting linguistic structures, such as parsing and chunking, has mostly relied on manual annotations of syntactic structures. This paper introduces an unsupervised approach to chunking, a syntactic task that i
Externí odkaz:
http://arxiv.org/abs/2309.04919
Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot main
Externí odkaz:
http://arxiv.org/abs/2304.07874
Autor:
Wu, You, Yang, Guangrui, Meng, Lize, Pan, Yiru, Zhang, Shenyan, Wu, Zijun, Zhao, Chu, Ren, Yue, Xu, Jingyang, Huang, Tao, Yang, Hao, Yu, Zhaoyuan, Yuan, Linwang, Liu, Hailong, Jiang, Qihao, Bian, Zihao, Zhou, Jian, Zhang, Zhigang, Huang, Changchun
Publikováno v:
In Water Research 1 January 2025 268 Part B
Autor:
Pan, Yiru, Meng, Lize, Wu, You, Zhang, Shenyan, Wu, Zijun, Zhao, Chu, Yang, Guangrui, Xu, Jingyang, Ren, Yue, Huang, Tao, Bian, Zihao, Jiang, Qihao, Zhou, Jian, Yang, Hao, Yu, Zhaoyuan, Yuan, Linwang, Liu, Hailong, Huang, Changchun
Publikováno v:
In Science of the Total Environment 10 November 2024 950