Zobrazeno 1 - 10
of 13 984
pro vyhledávání: '"A. P. See"'
Autor:
Jia, Xiaojun, Huang, Yihao, Liu, Yang, Tan, Peng Yan, Yau, Weng Kuan, Mak, Mun-Thye, Sim, Xin Ming, Ng, Wee Siong, Ng, See Kiong, Liu, Hanqing, Zhou, Lifeng, Yan, Huanqian, Sun, Xiaobing, Liu, Wei, Wang, Long, Qian, Yiming, Liu, Yong, Yang, Junxiao, Zhang, Zhexin, Lei, Leqi, Chen, Renmiao, Lu, Yida, Cui, Shiyao, Wang, Zizhou, Li, Shaohua, Wang, Yan, Goh, Rick Siow Mong, Zhen, Liangli, Zhang, Yingjie, Zhao, Zhe
This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms
Externí odkaz:
http://arxiv.org/abs/2411.14502
3D Gaussian Splatting (3DGS) has become a crucial method for acquiring 3D assets. To protect the copyright of these assets, digital watermarking techniques can be applied to embed ownership information discreetly within 3DGS models. However, existing
Externí odkaz:
http://arxiv.org/abs/2410.23718
Single-view 3D reconstruction methods like Triplane Gaussian Splatting (TGS) have enabled high-quality 3D model generation from just a single image input within seconds. However, this capability raises concerns about potential misuse, where malicious
Externí odkaz:
http://arxiv.org/abs/2410.22705
Autor:
Skøien, Jon Olav, Lampach, Nicolas, Ramos, Helena, Seljak, Rudolf, Koeble, Renate, See, Linda, van der Velde, Marijn
We develop a flexible approach by combining the Quadtree-based method with suppression to maximize the utility of the grid data and simultaneously to reduce the risk of disclosing private information from individual units. To protect data confidentia
Externí odkaz:
http://arxiv.org/abs/2410.17601
Autor:
Yee, Jeremy Stephen Gabriel, Ng, Pai Chet, Wang, Zhengkui, McLoughlin, Ian, Ng, Aik Beng, See, Simon
This paper presents a systematic review of the infrastructure requirements for deploying Large Language Models (LLMs) on-device within the context of small and medium-sized enterprises (SMEs), focusing on both hardware and software perspectives. From
Externí odkaz:
http://arxiv.org/abs/2410.16070
Autor:
Yan, Siyuan, Yu, Zhen, Primiero, Clare, Vico-Alonso, Cristina, Wang, Zhonghua, Yang, Litao, Tschandl, Philipp, Hu, Ming, Tan, Gin, Tang, Vincent, Ng, Aik Beng, Powell, David, Bonnington, Paul, See, Simon, Janda, Monika, Mar, Victoria, Kittler, Harald, Soyer, H. Peter, Ge, Zongyuan
Diagnosing and treating skin diseases require advanced visual skills across multiple domains and the ability to synthesize information from various imaging modalities. Current deep learning models, while effective at specific tasks such as diagnosing
Externí odkaz:
http://arxiv.org/abs/2410.15038
Autor:
Bukas, Christina, Subramanian, Harshavardhan, See, Fenja, Steinchen, Carina, Ezhov, Ivan, Boosarpu, Gowtham, Asgharpour, Sara, Burgstaller, Gerald, Lehmann, Mareike, Kofler, Florian, Piraud, Marie
High-throughput image analysis in the biomedical domain has gained significant attention in recent years, driving advancements in drug discovery, disease prediction, and personalized medicine. Organoids, specifically, are an active area of research,
Externí odkaz:
http://arxiv.org/abs/2410.14612
Unlike images and natural language tokens, time series data is highly semantically sparse, resulting in labor-intensive label annotations. Unsupervised and Semi-supervised Domain Adaptation (UDA and SSDA) have demonstrated efficiency in addressing th
Externí odkaz:
http://arxiv.org/abs/2410.06671
Autor:
Lin, Xinyu, Yang, Chaoqun, Wang, Wenjie, Li, Yongqi, Du, Cunxiao, Feng, Fuli, Ng, See-Kiong, Chua, Tat-Seng
Large Language Model (LLM)-based generative recommendation has achieved notable success, yet its practical deployment is costly particularly due to excessive inference latency caused by autoregressive decoding. For lossless LLM decoding acceleration,
Externí odkaz:
http://arxiv.org/abs/2410.05165
Autor:
Chan, Chunkit, Jiayang, Cheng, Liu, Xin, Yim, Yauwai, Jiang, Yuxin, Deng, Zheye, Li, Haoran, Song, Yangqiu, Wong, Ginny Y., See, Simon
Debate is the process of exchanging viewpoints or convincing others on a particular issue. Recent research has provided empirical evidence that the persuasiveness of an argument is determined not only by language usage but also by communicator charac
Externí odkaz:
http://arxiv.org/abs/2410.04239