Zobrazeno 1 - 10
of 3 957
pro vyhledávání: '"Xu, Ran"'
Autor:
Zhang, Jianguo, Lan, Tian, Zhu, Ming, Liu, Zuxin, Hoang, Thai, Kokane, Shirley, Yao, Weiran, Tan, Juntao, Prabhakar, Akshara, Chen, Haolin, Liu, Zhiwei, Feng, Yihao, Awalgaonkar, Tulika, Murthy, Rithesh, Hu, Eric, Chen, Zeyuan, Xu, Ran, Niebles, Juan Carlos, Heinecke, Shelby, Wang, Huan, Savarese, Silvio, Xiong, Caiming
Autonomous agents powered by large language models (LLMs) have attracted significant research interest. However, the open-source community faces many challenges in developing specialized models for agent tasks, driven by the scarcity of high-quality
Externí odkaz:
http://arxiv.org/abs/2409.03215
Autor:
Qin, Can, Xia, Congying, Ramakrishnan, Krithika, Ryoo, Michael, Tu, Lifu, Feng, Yihao, Shu, Manli, Zhou, Honglu, Awadalla, Anas, Wang, Jun, Purushwalkam, Senthil, Xue, Le, Zhou, Yingbo, Wang, Huan, Savarese, Silvio, Niebles, Juan Carlos, Chen, Zeyuan, Xu, Ran, Xiong, Caiming
We present xGen-VideoSyn-1, a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. Building on recent advancements, such as OpenAI's Sora, we explore the latent diffusion model (LDM) architecture and i
Externí odkaz:
http://arxiv.org/abs/2408.12590
Autor:
Xue, Le, Shu, Manli, Awadalla, Anas, Wang, Jun, Yan, An, Purushwalkam, Senthil, Zhou, Honglu, Prabhu, Viraj, Dai, Yutong, Ryoo, Michael S, Kendre, Shrikant, Zhang, Jieyu, Qin, Can, Zhang, Shu, Chen, Chia-Chih, Yu, Ning, Tan, Juntao, Awalgaonkar, Tulika Manoj, Heinecke, Shelby, Wang, Huan, Choi, Yejin, Schmidt, Ludwig, Chen, Zeyuan, Savarese, Silvio, Niebles, Juan Carlos, Xiong, Caiming, Xu, Ran
This report introduces xGen-MM (also known as BLIP-3), a framework for developing Large Multimodal Models (LMMs). The framework comprises meticulously curated datasets, a training recipe, model architectures, and a resulting suite of LMMs. xGen-MM, s
Externí odkaz:
http://arxiv.org/abs/2408.08872
Publikováno v:
Comptes Rendus. Mécanique, Vol 349, Iss 1, Pp 83-102 (2021)
Based on discrete element method (DEM), three kinds of soil–rock mixture (SRM) models with different coarse particle contents were established and triaxial compression tests were carried out. The results show that the force chains in the particle s
Externí odkaz:
https://doaj.org/article/e8d88f3db9eb478b8bfa1415b2b3eddd
Autor:
Shen, Jiaming, Xu, Ran, Jun, Yennie, Qin, Zhen, Liu, Tianqi, Yang, Carl, Liang, Yi, Baumgartner, Simon, Bendersky, Michael
Reward models (RMs) are crucial for aligning large language models (LLMs) with human preferences. They are trained using preference datasets where each example consists of one input prompt, two responses, and a preference label. As curating a high-qu
Externí odkaz:
http://arxiv.org/abs/2407.16008
This review examined the current advancements in data-driven methods for analyzing flow and transport in porous media, which has various applications in energy, chemical engineering, environmental science, and beyond. Although there has been progress
Externí odkaz:
http://arxiv.org/abs/2406.19939
Autor:
Awadalla, Anas, Xue, Le, Lo, Oscar, Shu, Manli, Lee, Hannah, Guha, Etash Kumar, Jordan, Matt, Shen, Sheng, Awadalla, Mohamed, Savarese, Silvio, Xiong, Caiming, Xu, Ran, Choi, Yejin, Schmidt, Ludwig
Multimodal interleaved datasets featuring free-form interleaved sequences of images and text are crucial for training frontier large multimodal models (LMMs). Despite the rapid progression of open-source LMMs, there remains a pronounced scarcity of l
Externí odkaz:
http://arxiv.org/abs/2406.11271
The growing availability of well-organized Electronic Health Records (EHR) data has enabled the development of various machine learning models towards disease risk prediction. However, existing risk prediction methods overlook the heterogeneity of co
Externí odkaz:
http://arxiv.org/abs/2406.10061
Autor:
Murthy, Rithesh, Yang, Liangwei, Tan, Juntao, Awalgaonkar, Tulika Manoj, Zhou, Yilun, Heinecke, Shelby, Desai, Sachin, Wu, Jason, Xu, Ran, Tan, Sarah, Zhang, Jianguo, Liu, Zhiwei, Kokane, Shirley, Liu, Zuxin, Zhu, Ming, Wang, Huan, Xiong, Caiming, Savarese, Silvio
The deployment of Large Language Models (LLMs) and Large Multimodal Models (LMMs) on mobile devices has gained significant attention due to the benefits of enhanced privacy, stability, and personalization. However, the hardware constraints of mobile
Externí odkaz:
http://arxiv.org/abs/2406.10290
Electronic Health Records (EHRs) contain rich patient information and are crucial for clinical research and practice. In recent years, deep learning models have been applied to EHRs, but they often rely on massive features, which may not be readily a
Externí odkaz:
http://arxiv.org/abs/2406.05682