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pro vyhledávání: '"HAN, Wei"'
Reconstructing desired objects and scenes has long been a primary goal in 3D computer vision. Single-view point cloud reconstruction has become a popular technique due to its low cost and accurate results. However, single-view reconstruction methods
Externí odkaz:
http://arxiv.org/abs/2412.09055
Recently, large language models (LLMs) have demonstrated impressive capabilities in dealing with new tasks with the help of in-context learning (ICL). In the study of Large Vision-Language Models (LVLMs), when implementing ICL, researchers usually ad
Externí odkaz:
http://arxiv.org/abs/2412.07619
Autor:
Li, Yikun, Zhang, Ting, Widyasari, Ratnadira, Tun, Yan Naing, Nguyen, Huu Hung, Bui, Tan, Irsan, Ivana Clairine, Cheng, Yiran, Lan, Xiang, Ang, Han Wei, Liauw, Frank, Weyssow, Martin, Kang, Hong Jin, Ouh, Eng Lieh, Shar, Lwin Khin, Lo, David
Accurate identification of software vulnerabilities is crucial for system integrity. Vulnerability datasets, often derived from the National Vulnerability Database (NVD) or directly from GitHub, are essential for training machine learning models to d
Externí odkaz:
http://arxiv.org/abs/2411.17274
When two or more energy bands become degenerate at a singular point in the momentum space, such singularity, or ``Dirac points", gives rise to intriguing quantum phenomena as well as unusual material properties. Systems at the Dirac points can posses
Externí odkaz:
http://arxiv.org/abs/2411.16287
Current face anonymization techniques often depend on identity loss calculated by face recognition models, which can be inaccurate and unreliable. Additionally, many methods require supplementary data such as facial landmarks and masks to guide the s
Externí odkaz:
http://arxiv.org/abs/2411.00762
The limited context window of contemporary large language models (LLMs) remains a huge barrier to their broader application across various domains. While continual pre-training on long-context data is a straightforward and effective solution, it incu
Externí odkaz:
http://arxiv.org/abs/2410.19318
Autor:
Xu, Yonghua, Wen, Zhigang, Yuan, Jianping, Wang, Zhen, Duan, Xuefeng, Wang, Na, Wang, Min, Wang, Hongguang, Rusul, Abdujappar, Hao, Longfei, Han, Wei
We have carried out a detailed study of individual pulse emission from the pulsar J1741$-$0840 (B1738$-$08), observed using the Parkes and Effelsberg radio telescopes at the $L$ band. The pulsar exhibits four emission components which are not well re
Externí odkaz:
http://arxiv.org/abs/2409.20128
Due to their substantial sizes, large language models (LLMs) are typically deployed within a single-backbone multi-tenant framework. In this setup, a single instance of an LLM backbone must cater to multiple users or tasks through the application of
Externí odkaz:
http://arxiv.org/abs/2409.17834
Federated learning (FL) has rapidly evolved as a promising paradigm that enables collaborative model training across distributed participants without exchanging their local data. Despite its broad applications in fields such as computer vision, graph
Externí odkaz:
http://arxiv.org/abs/2409.11509
Autor:
Qin, Meng, Zhang, Chaorui, Gao, Yu, Ding, Yibin, Jiang, Weipeng, Zhang, Weixi, Han, Wei, Bai, Bo
Graph partitioning (GP) is a classic problem that divides the node set of a graph into densely-connected blocks. Following the IEEE HPEC Graph Challenge and recent advances in pre-training techniques (e.g., large-language models), we propose PR-GPT (
Externí odkaz:
http://arxiv.org/abs/2409.00670