Zobrazeno 1 - 10
of 26 615
pro vyhledávání: '"Weijia An"'
Autor:
Xia, Peng, Zhu, Kangyu, Li, Haoran, Wang, Tianze, Shi, Weijia, Wang, Sheng, Zhang, Linjun, Zou, James, Yao, Huaxiu
Artificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in disease diagnosis and treatment planning. Recent progress in Medical Large Vision-Language Models (Med-LVLMs) has opened up new possibilities for inter
Externí odkaz:
http://arxiv.org/abs/2410.13085
Autor:
Ye, Junyan, Zhou, Baichuan, Huang, Zilong, Zhang, Junan, Bai, Tianyi, Kang, Hengrui, He, Jun, Lin, Honglin, Wang, Zihao, Wu, Tong, Wu, Zhizheng, Chen, Yiping, Lin, Dahua, He, Conghui, Li, Weijia
With the rapid development of AI-generated content, the future internet may be inundated with synthetic data, making the discrimination of authentic and credible multimodal data increasingly challenging. Synthetic data detection has thus garnered wid
Externí odkaz:
http://arxiv.org/abs/2410.09732
Real estate appraisal is important for a variety of endeavors such as real estate deals, investment analysis, and real property taxation. Recently, deep learning has shown great promise for real estate appraisal by harnessing substantial online trans
Externí odkaz:
http://arxiv.org/abs/2410.08947
Recent advancements in State Space Models, notably Mamba, have demonstrated superior performance over the dominant Transformer models, particularly in reducing the computational complexity from quadratic to linear. Yet, difficulties in adapting Mamba
Externí odkaz:
http://arxiv.org/abs/2410.06806
Humans have the ability to learn new tasks by inferring high-level concepts from existing solution, then manipulating these concepts in lieu of the raw data. Can we automate this process by deriving latent semantic structures in a document collection
Externí odkaz:
http://arxiv.org/abs/2410.05481
Accurate and timely modeling of labor migration is crucial for various urban governance and commercial tasks, such as local policy-making and business site selection. However, existing studies on labor migration largely rely on limited survey data wi
Externí odkaz:
http://arxiv.org/abs/2410.02639
Spatio-temporal forecasting is a critical component of various smart city applications, such as transportation optimization, energy management, and socio-economic analysis. Recently, several automated spatio-temporal forecasting methods have been pro
Externí odkaz:
http://arxiv.org/abs/2409.16586
The traditional data annotation process is often labor-intensive, time-consuming, and susceptible to human bias, which complicates the management of increasingly complex datasets. This study explores the potential of large language models (LLMs) as a
Externí odkaz:
http://arxiv.org/abs/2409.09615
Very low-resolution face recognition is challenging due to the serious loss of informative facial details in resolution degradation. In this paper, we propose a generative-discriminative representation distillation approach that combines generative r
Externí odkaz:
http://arxiv.org/abs/2409.06371
Autor:
Muennighoff, Niklas, Soldaini, Luca, Groeneveld, Dirk, Lo, Kyle, Morrison, Jacob, Min, Sewon, Shi, Weijia, Walsh, Pete, Tafjord, Oyvind, Lambert, Nathan, Gu, Yuling, Arora, Shane, Bhagia, Akshita, Schwenk, Dustin, Wadden, David, Wettig, Alexander, Hui, Binyuan, Dettmers, Tim, Kiela, Douwe, Farhadi, Ali, Smith, Noah A., Koh, Pang Wei, Singh, Amanpreet, Hajishirzi, Hannaneh
We introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE). OLMoE-1B-7B has 7 billion (B) parameters but uses only 1B per input token. We pretrain it on 5 trillion tokens and further adapt it to creat
Externí odkaz:
http://arxiv.org/abs/2409.02060