Zobrazeno 1 - 10
of 1 452
pro vyhledávání: '"Liu, Che"'
Autor:
Chen, Canyu, Yu, Jian, Chen, Shan, Liu, Che, Wan, Zhongwei, Bitterman, Danielle, Wang, Fei, Shu, Kai
Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams. Meanwhile, traditional ML models such as SVM and XGBoost have still
Externí odkaz:
http://arxiv.org/abs/2411.06469
Autor:
Gan, Ziliang, Lu, Yu, Zhang, Dong, Li, Haohan, Liu, Che, Liu, Jian, Liu, Ji, Wu, Haipang, Fu, Chaoyou, Xu, Zenglin, Zhang, Rongjunchen, Dai, Yong
In recent years, multimodal benchmarks for general domains have guided the rapid development of multimodal models on general tasks. However, the financial field has its peculiarities. It features unique graphical images (e.g., candlestick charts, tec
Externí odkaz:
http://arxiv.org/abs/2411.03314
Autor:
Liu, Che, Wan, Zhongwei, Wang, Haozhe, Chen, Yinda, Qaiser, Talha, Jin, Chen, Yousefi, Fariba, Burlutskiy, Nikolay, Arcucci, Rossella
Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling zero-shot tasks for medical image understanding. However, training MedVLP models typically requires large-scale datasets with paired, high-quality image-text data
Externí odkaz:
http://arxiv.org/abs/2410.13523
Autor:
Li, Jun, Aguirre, Aaron, Moura, Junior, Liu, Che, Zhong, Lanhai, Sun, Chenxi, Clifford, Gari, Westover, Brandon, Hong, Shenda
Artificial intelligence (AI) has demonstrated significant potential in ECG analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role in advancing medical AI. The development of an ECG foundation model h
Externí odkaz:
http://arxiv.org/abs/2410.04133
Advancements in Multimodal Large Language Models (MLLMs) have significantly improved medical task performance, such as Visual Question Answering (VQA) and Report Generation (RG). However, the fairness of these models across diverse demographic groups
Externí odkaz:
http://arxiv.org/abs/2410.01089
Role-playing is an emerging application in the field of Human-Computer Interaction (HCI), primarily implemented through the alignment training of a large language model (LLM) with assigned characters. Despite significant progress, role-playing agents
Externí odkaz:
http://arxiv.org/abs/2409.14710
Autor:
Wan, Zishen, Liu, Che-Kai, Yang, Hanchen, Raj, Ritik, Li, Chaojian, You, Haoran, Fu, Yonggan, Wan, Cheng, Li, Sixu, Kim, Youbin, Samajdar, Ananda, Lin, Yingyan Celine, Ibrahim, Mohamed, Rabaey, Jan M., Krishna, Tushar, Raychowdhury, Arijit
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational trajectories, limited robustness, and a lack of explainability. To develop next-gener
Externí odkaz:
http://arxiv.org/abs/2409.13153
How Does Diverse Interpretability of Textual Prompts Impact Medical Vision-Language Zero-Shot Tasks?
Recent advancements in medical vision-language pre-training (MedVLP) have significantly enhanced zero-shot medical vision tasks such as image classification by leveraging large-scale medical image-text pair pre-training. However, the performance of t
Externí odkaz:
http://arxiv.org/abs/2409.00543
Data assimilation techniques are often confronted with challenges handling complex high dimensional physical systems, because high precision simulation in complex high dimensional physical systems is computationally expensive and the exact observatio
Externí odkaz:
http://arxiv.org/abs/2409.00244
The rapid computation of electromagnetic (EM) fields across various scenarios has long been a challenge, primarily due to the need for precise geometric models. The emergence of point cloud data offers a potential solution to this issue. However, the
Externí odkaz:
http://arxiv.org/abs/2408.15583