Zobrazeno 1 - 10
of 644
pro vyhledávání: '"Huang Kexin"'
Autor:
Sun, Jie, Zhang, Tianyu, Jiang, Houcheng, Huang, Kexin, Luo, Chi, Wu, Junkang, Wu, Jiancan, Zhang, An, Wang, Xiang
Auctions, a fundamental economic mechanism, encompass the valuation of goods or services and the competitive bidding algorithms within a specific framework, serving to uncover the true market value. However, current research predominantly focuses on
Externí odkaz:
http://arxiv.org/abs/2410.15817
Visual brain decoding aims to decode visual information from human brain activities. Despite the great progress, one critical limitation of current brain decoding research lies in the lack of generalization capability to unseen subjects. Prior works
Externí odkaz:
http://arxiv.org/abs/2410.14445
Large Language Models (LLMs) can memorize sensitive information, raising concerns about potential misuse. LLM Unlearning, a post-hoc approach to remove this information from trained LLMs, offers a promising solution to mitigate these risks. However,
Externí odkaz:
http://arxiv.org/abs/2409.11844
Autor:
Robinson, Joshua, Ranjan, Rishabh, Hu, Weihua, Huang, Kexin, Han, Jiaqi, Dobles, Alejandro, Fey, Matthias, Lenssen, Jan E., Yuan, Yiwen, Zhang, Zecheng, He, Xinwei, Leskovec, Jure
We present RelBench, a public benchmark for solving predictive tasks over relational databases with graph neural networks. RelBench provides databases and tasks spanning diverse domains and scales, and is intended to be a foundational infrastructure
Externí odkaz:
http://arxiv.org/abs/2407.20060
Autor:
Chen, Jintai, Hu, Yaojun, Wang, Yue, Lu, Yingzhou, Cao, Xu, Lin, Miao, Xu, Hongxia, Wu, Jian, Xiao, Cao, Sun, Jimeng, Glass, Lucas, Huang, Kexin, Zitnik, Marinka, Fu, Tianfan
Clinical trials are pivotal for developing new medical treatments, yet they typically pose some risks such as patient mortality, adverse events, and enrollment failure that waste immense efforts spanning over a decade. Applying artificial intelligenc
Externí odkaz:
http://arxiv.org/abs/2407.00631
Autor:
Zhao, Haiquan, Li, Lingyu, Chen, Shisong, Kong, Shuqi, Wang, Jiaan, Huang, Kexin, Gu, Tianle, Wang, Yixu, Jian, Wang, Liang, Dandan, Li, Zhixu, Teng, Yan, Xiao, Yanghua, Wang, Yingchun
Emotion Support Conversation (ESC) is a crucial application, which aims to reduce human stress, offer emotional guidance, and ultimately enhance human mental and physical well-being. With the advancement of Large Language Models (LLMs), many research
Externí odkaz:
http://arxiv.org/abs/2406.14952
Autor:
Wu, Shirley, Zhao, Shiyu, Huang, Qian, Huang, Kexin, Yasunaga, Michihiro, Cao, Kaidi, Ioannidis, Vassilis N., Subbian, Karthik, Leskovec, Jure, Zou, James
Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and reduce hallucinations. However, developing prompting techniques that enable LLM agents to effectively use thes
Externí odkaz:
http://arxiv.org/abs/2406.11200
Autor:
Chen, Ping, Zhang, Wenjie, He, Shuibing, Gu, Yingjie, Peng, Zhuwei, Huang, Kexin, Zhan, Xuan, Chen, Weijian, Zheng, Yi, Wang, Zhefeng, Yin, Yanlong, Chen, Gang
Large model training has been using recomputation to alleviate the memory pressure and pipelining to exploit the parallelism of data, tensor, and devices. The existing recomputation approaches may incur up to 40% overhead when training real-world mod
Externí odkaz:
http://arxiv.org/abs/2406.08756
Autor:
Gu, Tianle, Zhou, Zeyang, Huang, Kexin, Liang, Dandan, Wang, Yixu, Zhao, Haiquan, Yao, Yuanqi, Qiao, Xingge, Wang, Keqing, Yang, Yujiu, Teng, Yan, Qiao, Yu, Wang, Yingchun
Powered by remarkable advancements in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) demonstrate impressive capabilities in manifold tasks. However, the practical application scenarios of MLLMs are intricate, exposing them to
Externí odkaz:
http://arxiv.org/abs/2406.07594
Autor:
Roohani, Yusuf, Lee, Andrew, Huang, Qian, Vora, Jian, Steinhart, Zachary, Huang, Kexin, Marson, Alexander, Liang, Percy, Leskovec, Jure
Agents based on large language models have shown great potential in accelerating scientific discovery by leveraging their rich background knowledge and reasoning capabilities. In this paper, we introduce BioDiscoveryAgent, an agent that designs new e
Externí odkaz:
http://arxiv.org/abs/2405.17631