Zobrazeno 1 - 10
of 1 127
pro vyhledávání: '"WANG Tianyang"'
Autor:
Wang, Tianyang, Liu, Ming, Peng, Benji, Song, Xinyuan, Zhang, Charles, Sun, Xintian, Niu, Qian, Liu, Junyu, Chen, Silin, Chen, Keyu, Li, Ming, Feng, Pohsun, Bi, Ziqian, Wang, Yunze, Zhang, Yichao, Fei, Cheng, Yan, Lawrence KQ
Clinical trials are an indispensable part of the drug development process, bridging the gap between basic research and clinical application. During the development of new drugs, clinical trials are used not only to evaluate the safety and efficacy of
Externí odkaz:
http://arxiv.org/abs/2412.09378
Autor:
Wang, Tianyang, Bi, Ziqian, Zhang, Yichao, Liu, Ming, Hsieh, Weiche, Feng, Pohsun, Yan, Lawrence K. Q., Wen, Yizhu, Peng, Benji, Liu, Junyu, Chen, Keyu, Zhang, Sen, Li, Ming, Jiang, Chuanqi, Song, Xinyuan, Yang, Junjie, Jing, Bowen, Ren, Jintao, Song, Junhao, Tseng, Hong-Ming, Chen, Silin, Wang, Yunze, Liang, Chia Xin, Xu, Jiawei, Pan, Xuanhe, Wang, Jinlang, Niu, Qian
Deep learning has transformed AI applications but faces critical security challenges, including adversarial attacks, data poisoning, model theft, and privacy leakage. This survey examines these vulnerabilities, detailing their mechanisms and impact o
Externí odkaz:
http://arxiv.org/abs/2412.08969
Autor:
Peng, Benji, Liang, Chia Xin, Bi, Ziqian, Liu, Ming, Zhang, Yichao, Wang, Tianyang, Chen, Keyu, Song, Xinyuan, Feng, Pohsun
Deep learning-based image generation has undergone a paradigm shift since 2021, marked by fundamental architectural breakthroughs and computational innovations. Through reviewing architectural innovations and empirical results, this paper analyzes th
Externí odkaz:
http://arxiv.org/abs/2412.09656
Autor:
Hsieh, Weiche, Bi, Ziqian, Chen, Keyu, Peng, Benji, Zhang, Sen, Xu, Jiawei, Wang, Jinlang, Yin, Caitlyn Heqi, Zhang, Yichao, Feng, Pohsun, Wen, Yizhu, Wang, Tianyang, Li, Ming, Liang, Chia Xin, Ren, Jintao, Niu, Qian, Chen, Silin, Yan, Lawrence K. Q., Xu, Han, Tseng, Hong-Ming, Song, Xinyuan, Jing, Bowen, Yang, Junjie, Song, Junhao, Liu, Junyu, Liu, Ming
Advancements in artificial intelligence, machine learning, and deep learning have catalyzed the transformation of big data analytics and management into pivotal domains for research and application. This work explores the theoretical foundations, met
Externí odkaz:
http://arxiv.org/abs/2412.02187
Autor:
Hsieh, Weiche, Bi, Ziqian, Jiang, Chuanqi, Liu, Junyu, Peng, Benji, Zhang, Sen, Pan, Xuanhe, Xu, Jiawei, Wang, Jinlang, Chen, Keyu, Feng, Pohsun, Wen, Yizhu, Song, Xinyuan, Wang, Tianyang, Liu, Ming, Yang, Junjie, Li, Ming, Jing, Bowen, Ren, Jintao, Song, Junhao, Tseng, Hong-Ming, Zhang, Yichao, Yan, Lawrence K. Q., Niu, Qian, Chen, Silin, Wang, Yunze, Liang, Chia Xin
Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making processes. This book offers a comprehensive guide to XAI, bridging foundati
Externí odkaz:
http://arxiv.org/abs/2412.00800
Autor:
Liang, Chia Xin, Tian, Pu, Yin, Caitlyn Heqi, Yua, Yao, An-Hou, Wei, Ming, Li, Wang, Tianyang, Bi, Ziqian, Liu, Ming
This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models. Starting with foundational concepts, we
Externí odkaz:
http://arxiv.org/abs/2411.06284
Autor:
Zhang, Charles, Peng, Benji, Sun, Xintian, Niu, Qian, Liu, Junyu, Chen, Keyu, Li, Ming, Feng, Pohsun, Bi, Ziqian, Liu, Ming, Zhang, Yichao, Fei, Cheng, Yin, Caitlyn Heqi, Yan, Lawrence KQ, Wang, Tianyang
Word embeddings and language models have transformed natural language processing (NLP) by facilitating the representation of linguistic elements in continuous vector spaces. This review visits foundational concepts such as the distributional hypothes
Externí odkaz:
http://arxiv.org/abs/2411.05036
Autor:
Chen, Keyu, Fei, Cheng, Bi, Ziqian, Liu, Junyu, Peng, Benji, Zhang, Sen, Pan, Xuanhe, Xu, Jiawei, Wang, Jinlang, Yin, Caitlyn Heqi, Zhang, Yichao, Feng, Pohsun, Wen, Yizhu, Wang, Tianyang, Li, Ming, Ren, Jintao, Niu, Qian, Chen, Silin, Hsieh, Weiche, Yan, Lawrence K. Q., Liang, Chia Xin, Xu, Han, Tseng, Hong-Ming, Song, Xinyuan, Liu, Ming
With a focus on natural language processing (NLP) and the role of large language models (LLMs), we explore the intersection of machine learning, deep learning, and artificial intelligence. As artificial intelligence continues to revolutionize fields
Externí odkaz:
http://arxiv.org/abs/2411.05026
Autor:
Yan, Lawrence K. Q., Niu, Qian, Li, Ming, Zhang, Yichao, Yin, Caitlyn Heqi, Fei, Cheng, Peng, Benji, Bi, Ziqian, Feng, Pohsun, Chen, Keyu, Wang, Tianyang, Wang, Yunze, Chen, Silin, Liu, Ming, Liu, Junyu
With the increasing application of large language models (LLMs) in the medical domain, evaluating these models' performance using benchmark datasets has become crucial. This paper presents a comprehensive survey of various benchmark datasets employed
Externí odkaz:
http://arxiv.org/abs/2410.21348
Autor:
Hsieh, Weiche, Bi, Ziqian, Liu, Junyu, Peng, Benji, Zhang, Sen, Pan, Xuanhe, Xu, Jiawei, Wang, Jinlang, Chen, Keyu, Yin, Caitlyn Heqi, Feng, Pohsun, Wen, Yizhu, Wang, Tianyang, Li, Ming, Ren, Jintao, Niu, Qian, Chen, Silin, Liu, Ming
Digital Signal Processing (DSP) and Digital Image Processing (DIP) with Machine Learning (ML) and Deep Learning (DL) are popular research areas in Computer Vision and related fields. We highlight transformative applications in image enhancement, filt
Externí odkaz:
http://arxiv.org/abs/2410.20304