Zobrazeno 1 - 10
of 11 952
pro vyhledávání: '"An, Zifeng"'
A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges
Autor:
Wang, Zifeng, Wang, Hanyin, Danek, Benjamin, Li, Ying, Mack, Christina, Poon, Hoifung, Wang, Yajun, Rajpurkar, Pranav, Sun, Jimeng
The integration of Large Language Models (LLMs) into medical applications has sparked widespread interest across the healthcare industry, from drug discovery and development to clinical decision support, assisting telemedicine, medical devices, and h
Externí odkaz:
http://arxiv.org/abs/2411.00024
Data science plays a critical role in clinical research, but it requires professionals with expertise in coding and medical data analysis. Large language models (LLMs) have shown great potential in supporting medical tasks and performing well in gene
Externí odkaz:
http://arxiv.org/abs/2410.21591
Autor:
Hou, Qiqi, Rauwendaal, Randall, Li, Zifeng, Le, Hoang, Farhadzadeh, Farzad, Porikli, Fatih, Bourd, Alexei, Said, Amir
Recently, 3D Gaussian Splatting (3DGS) has emerged as a significant advancement in 3D scene reconstruction, attracting considerable attention due to its ability to recover high-fidelity details while maintaining low complexity. Despite the promising
Externí odkaz:
http://arxiv.org/abs/2410.18931
Autor:
Jin, Qiao, Wan, Nicholas, Leaman, Robert, Tian, Shubo, Wang, Zhizheng, Yang, Yifan, Wang, Zifeng, Xiong, Guangzhi, Lai, Po-Ting, Zhu, Qingqing, Hou, Benjamin, Sarfo-Gyamfi, Maame, Zhang, Gongbo, Gilson, Aidan, Bhasuran, Balu, He, Zhe, Zhang, Aidong, Sun, Jimeng, Weng, Chunhua, Summers, Ronald M., Chen, Qingyu, Peng, Yifan, Lu, Zhiyong
Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare by generating human-like responses across diverse contexts and adapting to novel tasks following human instructions. Th
Externí odkaz:
http://arxiv.org/abs/2410.18856
Multimodal Large Language Models (MLLMs) have demonstrated impressive abilities across various tasks, including visual question answering and chart comprehension, yet existing benchmarks for chart-related tasks fall short in capturing the complexity
Externí odkaz:
http://arxiv.org/abs/2410.14179
Autor:
Chen, Lichang, Hu, Hexiang, Zhang, Mingda, Chen, Yiwen, Wang, Zifeng, Li, Yandong, Shyam, Pranav, Zhou, Tianyi, Huang, Heng, Yang, Ming-Hsuan, Gong, Boqing
We introduce OmnixR, an evaluation suite designed to benchmark SoTA Omni-modality Language Models, such as GPT-4o and Gemini. Evaluating OLMs, which integrate multiple modalities such as text, vision, and audio, presents unique challenges. Particular
Externí odkaz:
http://arxiv.org/abs/2410.12219
Autor:
Xu, Wenda, Han, Rujun, Wang, Zifeng, Le, Long T., Madeka, Dhruv, Li, Lei, Wang, William Yang, Agarwal, Rishabh, Lee, Chen-Yu, Pfister, Tomas
Recent advances in knowledge distillation (KD) have enabled smaller student models to approach the performance of larger teacher models. However, popular methods such as supervised KD and on-policy KD, are adversely impacted by the knowledge gaps bet
Externí odkaz:
http://arxiv.org/abs/2410.11325
Autor:
Feng, Shangbin, Wang, Zifeng, Wang, Yike, Ebrahimi, Sayna, Palangi, Hamid, Miculicich, Lesly, Kulshrestha, Achin, Rauschmayr, Nathalie, Choi, Yejin, Tsvetkov, Yulia, Lee, Chen-Yu, Pfister, Tomas
We propose Model Swarms, a collaborative search algorithm to adapt LLMs via swarm intelligence, the collective behavior guiding individual systems. Specifically, Model Swarms starts with a pool of LLM experts and a utility function. Guided by the bes
Externí odkaz:
http://arxiv.org/abs/2410.11163
3D biometric techniques on finger traits have become a new trend and have demonstrated a powerful ability for recognition and anti-counterfeiting. Existing methods follow an explicit 3D pipeline that reconstructs the models first and then extracts fe
Externí odkaz:
http://arxiv.org/abs/2410.09582
A novel class of advanced algorithms, termed Goal-Conditioned Weighted Supervised Learning (GCWSL), has recently emerged to tackle the challenges posed by sparse rewards in goal-conditioned reinforcement learning (RL). GCWSL consistently delivers str
Externí odkaz:
http://arxiv.org/abs/2410.06648