Zobrazeno 1 - 10
of 7 988
pro vyhledávání: '"Zhu, Qi"'
Autor:
Xu, Jiayang, Li, Yamin, Su, Ruxin, Wu, Saishuang, Wu, Chengcheng, Wang, Haiwa, Zhu, Qi, Fang, Yue, Jiang, Fan, Tong, Shanbao, Zhang, Yunting, Guo, Xiaoli
Mother-child interaction is a highly dynamic process neurally characterized by inter-brain synchrony (IBS) at {\theta} and/or {\alpha} rhythms. However, their establishment, dynamic changes, and roles in mother-child interactions remain unknown. Thro
Externí odkaz:
http://arxiv.org/abs/2410.13669
Autor:
Min, Chen, Si, Shubin, Wang, Xu, Xue, Hanzhang, Jiang, Weizhong, Liu, Yang, Wang, Juan, Zhu, Qingtian, Zhu, Qi, Luo, Lun, Kong, Fanjie, Miao, Jinyu, Cai, Xudong, An, Shuai, Li, Wei, Mei, Jilin, Sun, Tong, Zhai, Heng, Liu, Qifeng, Zhao, Fangzhou, Chen, Liang, Wang, Shuai, Shang, Erke, Shang, Linzhi, Zhao, Kunlong, Li, Fuyang, Fu, Hao, Jin, Lei, Zhao, Jian, Mao, Fangyuan, Xiao, Zhipeng, Li, Chengyang, Dai, Bin, Zhao, Dawei, Xiao, Liang, Nie, Yiming, Hu, Yu, Li, Xuelong
Research on autonomous driving in unstructured outdoor environments is less advanced than in structured urban settings due to challenges like environmental diversities and scene complexity. These environments-such as rural areas and rugged terrains-p
Externí odkaz:
http://arxiv.org/abs/2410.07701
Model-Based Reward Shaping for Adversarial Inverse Reinforcement Learning in Stochastic Environments
Autor:
Zhan, Simon Sinong, Wu, Qingyuan, Wang, Philip, Wang, Yixuan, Jiao, Ruochen, Huang, Chao, Zhu, Qi
In this paper, we aim to tackle the limitation of the Adversarial Inverse Reinforcement Learning (AIRL) method in stochastic environments where theoretical results cannot hold and performance is degraded. To address this issue, we propose a novel met
Externí odkaz:
http://arxiv.org/abs/2410.03847
Neural networks are increasingly used in safety-critical applications such as robotics and autonomous vehicles. However, the deployment of neural-network-controlled systems (NNCSs) raises significant safety concerns. Many recent advances overlook cri
Externí odkaz:
http://arxiv.org/abs/2408.08592
While LLMs have demonstrated impressive performance across various domains and tasks, their security issues have become increasingly severe. Machine unlearning (MU) has emerged as a promising solution to address these issues by removing the influence
Externí odkaz:
http://arxiv.org/abs/2407.10223
Diffusion models have made remarkable progress in solving various inverse problems, attributing to the generative modeling capability of the data manifold. Posterior sampling from the conditional score function enable the precious data consistency ce
Externí odkaz:
http://arxiv.org/abs/2407.09768
The rapid development of spatial transcriptomics(ST) enables the measurement of gene expression at spatial resolution, making it possible to simultaneously profile the gene expression, spatial locations of spots, and the matched histopathological ima
Externí odkaz:
http://arxiv.org/abs/2406.12229
Autor:
Zhang, Shichang, Zheng, Da, Zhang, Jiani, Zhu, Qi, song, Xiang, Adeshina, Soji, Faloutsos, Christos, Karypis, George, Sun, Yizhou
Text-rich graphs, prevalent in data mining contexts like e-commerce and academic graphs, consist of nodes with textual features linked by various relations. Traditional graph machine learning models, such as Graph Neural Networks (GNNs), excel in enc
Externí odkaz:
http://arxiv.org/abs/2406.11884
Most existing speech disfluency detection techniques only rely upon acoustic data. In this work, we present a practical multimodal disfluency detection approach that leverages available video data together with audio. We curate an audiovisual dataset
Externí odkaz:
http://arxiv.org/abs/2406.06964
Autor:
Zheng, Da, Song, Xiang, Zhu, Qi, Zhang, Jian, Vasiloudis, Theodore, Ma, Runjie, Zhang, Houyu, Wang, Zichen, Adeshina, Soji, Nisa, Israt, Mottini, Alejandro, Cui, Qingjun, Rangwala, Huzefa, Zeng, Belinda, Faloutsos, Christos, Karypis, George
Publikováno v:
KDD 2024
Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution
Externí odkaz:
http://arxiv.org/abs/2406.06022