Zobrazeno 1 - 10
of 698 196
pro vyhledávání: '"A. Tong-on"'
Autor:
Shen, Xuan, Song, Zhao, Zhou, Yufa, Chen, Bo, Liu, Jing, Zhang, Ruiyi, Rossi, Ryan A., Tan, Hao, Yu, Tong, Chen, Xiang, Zhou, Yufan, Sun, Tong, Zhao, Pu, Wang, Yanzhi, Gu, Jiuxiang
Transformers have emerged as the leading architecture in deep learning, proving to be versatile and highly effective across diverse domains beyond language and image processing. However, their impressive performance often incurs high computational co
Externí odkaz:
http://arxiv.org/abs/2412.12441
Autor:
Chen, Zhe, Wang, Weiyun, Cao, Yue, Liu, Yangzhou, Gao, Zhangwei, Cui, Erfei, Zhu, Jinguo, Ye, Shenglong, Tian, Hao, Liu, Zhaoyang, Gu, Lixin, Wang, Xuehui, Li, Qingyun, Ren, Yimin, Chen, Zixuan, Luo, Jiapeng, Wang, Jiahao, Jiang, Tan, Wang, Bo, He, Conghui, Shi, Botian, Zhang, Xingcheng, Lv, Han, Wang, Yi, Shao, Wenqi, Chu, Pei, Tu, Zhongying, He, Tong, Wu, Zhiyong, Deng, Huipeng, Ge, Jiaye, Chen, Kai, Dou, Min, Lu, Lewei, Zhu, Xizhou, Lu, Tong, Lin, Dahua, Qiao, Yu, Dai, Jifeng, Wang, Wenhai
We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data
Externí odkaz:
http://arxiv.org/abs/2412.05271
Autor:
Shan, Weiqiao, Meng, Long, Zheng, Tong, Luo, Yingfeng, Li, Bei, Wang, junxin, Xiao, Tong, Zhu, Jingbo
Large language models (LLMs) exhibit exceptional performance across various downstream tasks. However, they encounter limitations due to slow inference speeds stemming from their extensive parameters. The early exit (EE) is an approach that aims to a
Externí odkaz:
http://arxiv.org/abs/2412.01455
Autor:
Li, Bei, Zheng, Tong, Wang, Rui, Liu, Jiahao, Guo, Qingyan, Guo, Junliang, Tan, Xu, Xiao, Tong, Zhu, Jingbo, Wang, Jingang, Cai, Xunliang
Residual networks, as discrete approximations of Ordinary Differential Equations (ODEs), have inspired significant advancements in neural network design, including multistep methods, high-order methods, and multi-particle dynamical systems. The preci
Externí odkaz:
http://arxiv.org/abs/2411.03042
Autor:
Chen, Jian, Zhang, Ruiyi, Zhou, Yufan, Yu, Tong, Dernoncourt, Franck, Gu, Jiuxiang, Rossi, Ryan A., Chen, Changyou, Sun, Tong
Large multimodal models (LMMs) have recently shown great progress in text-rich image understanding, yet they still struggle with complex, multi-page, visually-rich documents. Traditional methods using document parsers for retrieval-augmented generati
Externí odkaz:
http://arxiv.org/abs/2411.01106
In clinical trials, the observation of participant outcomes may frequently be hindered by death, leading to ambiguity in defining a scientifically meaningful final outcome for those who die. Principal stratification methods are valuable tools for add
Externí odkaz:
http://arxiv.org/abs/2410.07483
Autor:
Zhang, Di, Wu, Jianbo, Lei, Jingdi, Che, Tong, Li, Jiatong, Xie, Tong, Huang, Xiaoshui, Zhang, Shufei, Pavone, Marco, Li, Yuqiang, Ouyang, Wanli, Zhou, Dongzhan
This paper presents an advanced mathematical problem-solving framework, LLaMA-Berry, for enhancing the mathematical reasoning ability of Large Language Models (LLMs). The framework combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to
Externí odkaz:
http://arxiv.org/abs/2410.02884
Despite advances in AI's performance and interpretability, AI advisors can undermine experts' decisions and increase the time and effort experts must invest to make decisions. Consequently, AI systems deployed in high-stakes settings often fail to co
Externí odkaz:
http://arxiv.org/abs/2412.19530
Autor:
Ranathungaa, Surangika, Nayak, Shravan, Huang, Shih-Ting Cindy, Mao, Yanke, Su, Tong, Chan, Yun-Hsiang Ray, Yuan, Songchen, Rinaldi, Anthony, Lee, Annie En-Shiun
Neural Machine Translation (NMT) systems built on multilingual sequence-to-sequence Language Models (msLMs) fail to deliver expected results when the amount of parallel data for a language, as well as the language's representation in the model are li
Externí odkaz:
http://arxiv.org/abs/2412.19522
This paper presents an optimization framework for the joint multimodal transit frequency and shared autonomous vehicle (SAV) fleet size optimization, a problem variant of the transit network frequency setting problem (TNFSP) that explicitly considers
Externí odkaz:
http://arxiv.org/abs/2412.19401