Zobrazeno 1 - 10
of 3 819
pro vyhledávání: '"Zhang Ce"'
Autor:
Kan, Zhehan, Zhang, Ce, Liao, Zihan, Tian, Yapeng, Yang, Wenming, Xiao, Junyuan, Li, Xu, Jiang, Dongmei, Wang, Yaowei, Liao, Qingmin
Large Vision-Language Model (LVLM) systems have demonstrated impressive vision-language reasoning capabilities but suffer from pervasive and severe hallucination issues, posing significant risks in critical domains such as healthcare and autonomous s
Externí odkaz:
http://arxiv.org/abs/2411.12713
Autor:
Weber, Maurice, Fu, Daniel, Anthony, Quentin, Oren, Yonatan, Adams, Shane, Alexandrov, Anton, Lyu, Xiaozhong, Nguyen, Huu, Yao, Xiaozhe, Adams, Virginia, Athiwaratkun, Ben, Chalamala, Rahul, Chen, Kezhen, Ryabinin, Max, Dao, Tri, Liang, Percy, Ré, Christopher, Rish, Irina, Zhang, Ce
Large language models are increasingly becoming a cornerstone technology in artificial intelligence, the sciences, and society as a whole, yet the optimal strategies for dataset composition and filtering remain largely elusive. Many of the top-perfor
Externí odkaz:
http://arxiv.org/abs/2411.12372
Test-time adaptation, which enables models to generalize to diverse data with unlabeled test samples, holds significant value in real-world scenarios. Recently, researchers have applied this setting to advanced pre-trained vision-language models (VLM
Externí odkaz:
http://arxiv.org/abs/2410.12790
Autor:
Veitch-Michaelis, Josh, Cottam, Andrew, Schweizer, Daniella, Broadbent, Eben N., Dao, David, Zhang, Ce, Zambrano, Angelica Almeyda, Max, Simeon
Accurately quantifying tree cover is an important metric for ecosystem monitoring and for assessing progress in restored sites. Recent works have shown that deep learning-based segmentation algorithms are capable of accurately mapping trees at countr
Externí odkaz:
http://arxiv.org/abs/2407.11743
Autor:
Alexandrov, Anton, Raychev, Veselin, Müller, Mark Niklas, Zhang, Ce, Vechev, Martin, Toutanova, Kristina
As open-weight large language models (LLMs) achieve ever more impressive performances across a wide range of tasks in English, practitioners aim to adapt these models to different languages. However, such language adaptation is often accompanied by c
Externí odkaz:
http://arxiv.org/abs/2407.08699
Autor:
Zhang, Ce, Eskandarian, Azim
LiDAR is one of the most crucial sensors for autonomous vehicle perception. However, current LiDAR-based point cloud perception algorithms lack comprehensive and rigorous LiDAR quality assessment methods, leading to uncertainty in detection performan
Externí odkaz:
http://arxiv.org/abs/2406.17265
Recent advances in large language models (LLMs) demonstrate substantial capabilities in natural language understanding and generation tasks. With the growing number of LLMs, how to harness the collective expertise of multiple LLMs is an exciting open
Externí odkaz:
http://arxiv.org/abs/2406.04692
Autor:
Chen, Qi, Geng, Xiubo, Rosset, Corby, Buractaon, Carolyn, Lu, Jingwen, Shen, Tao, Zhou, Kun, Xiong, Chenyan, Gong, Yeyun, Bennett, Paul, Craswell, Nick, Xie, Xing, Yang, Fan, Tower, Bryan, Rao, Nikhil, Dong, Anlei, Jiang, Wenqi, Liu, Zheng, Li, Mingqin, Liu, Chuanjie, Li, Zengzhong, Majumder, Rangan, Neville, Jennifer, Oakley, Andy, Risvik, Knut Magne, Simhadri, Harsha Vardhan, Varma, Manik, Wang, Yujing, Yang, Linjun, Yang, Mao, Zhang, Ce
Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-scale information-rich web dataset, featuring millions of real clicked
Externí odkaz:
http://arxiv.org/abs/2405.07526
Autor:
Rhyner, Steve, Luo, Haocong, Gómez-Luna, Juan, Sadrosadati, Mohammad, Jiang, Jiawei, Olgun, Ataberk, Gupta, Harshita, Zhang, Ce, Mutlu, Onur
Modern Machine Learning (ML) training on large-scale datasets is a very time-consuming workload. It relies on the optimization algorithm Stochastic Gradient Descent (SGD) due to its effectiveness, simplicity, and generalization performance. Processor
Externí odkaz:
http://arxiv.org/abs/2404.07164
Autor:
Poli, Michael, Thomas, Armin W, Nguyen, Eric, Ponnusamy, Pragaash, Deiseroth, Björn, Kersting, Kristian, Suzuki, Taiji, Hie, Brian, Ermon, Stefano, Ré, Christopher, Zhang, Ce, Massaroli, Stefano
The development of deep learning architectures is a resource-demanding process, due to a vast design space, long prototyping times, and high compute costs associated with at-scale model training and evaluation. We set out to simplify this process by
Externí odkaz:
http://arxiv.org/abs/2403.17844