Zobrazeno 1 - 10
of 25 761
pro vyhledávání: '"wang, kun"'
Autor:
Zhang, Yi-Fan, Zhang, Huanyu, Tian, Haochen, Fu, Chaoyou, Zhang, Shuangqing, Wu, Junfei, Li, Feng, Wang, Kun, Wen, Qingsong, Zhang, Zhang, Wang, Liang, Jin, Rong, Tan, Tieniu
Comprehensive evaluation of Multimodal Large Language Models (MLLMs) has recently garnered widespread attention in the research community. However, we observe that existing benchmarks present several common barriers that make it difficult to measure
Externí odkaz:
http://arxiv.org/abs/2408.13257
Autor:
Liu, Chenyu, Zhou, Xinliang, Wu, Yihao, Ding, Yi, Zhai, Liming, Wang, Kun, Jia, Ziyu, Liu, Yang
Compared to other modalities, electroencephalogram (EEG) based emotion recognition can intuitively respond to emotional patterns in the human brain and, therefore, has become one of the most focused tasks in affective computing. The nature of emotion
Externí odkaz:
http://arxiv.org/abs/2408.06027
This work develops a robust and efficient framework of the adjoint gradient-based aerodynamic shape optimization (ASO) using high-order discontinuous Galerkin methods (DGMs) as the CFD solver. The adjoint-enabled gradients based on different CFD solv
Externí odkaz:
http://arxiv.org/abs/2407.18963
Although video perception models have made remarkable advancements in recent years, they still heavily rely on explicit text descriptions or pre-defined categories to identify target instances before executing video perception tasks. These models, ho
Externí odkaz:
http://arxiv.org/abs/2407.14500
Autor:
Wang, Kun, Poskitt, Christopher M., Sun, Yang, Sun, Jun, Wang, Jingyi, Cheng, Peng, Chen, Jiming
Autonomous Vehicles (AVs) rely on sophisticated Autonomous Driving Systems (ADSs) to provide passengers a satisfying and safe journey. The individual preferences of riders plays a crucial role in shaping the perception of safety and comfort while the
Externí odkaz:
http://arxiv.org/abs/2407.13201
In this study, we introduce the More-Interaction Particle Transformer (MIParT), a novel deep learning neural network designed for jet tagging. This framework incorporates our own design, the More-Interaction Attention (MIA) mechanism, which increases
Externí odkaz:
http://arxiv.org/abs/2407.08682
Autor:
Chang, Kaiyan, Chen, Zhirong, Zhou, Yunhao, Zhu, Wenlong, wang, kun, Xu, Haobo, Li, Cangyuan, Wang, Mengdi, Liang, Shengwen, Li, Huawei, Han, Yinhe, Wang, Ying
Natural language interfaces have exhibited considerable potential in the automation of Verilog generation derived from high-level specifications through the utilization of large language models, garnering significant attention. Nevertheless, this pap
Externí odkaz:
http://arxiv.org/abs/2407.08473
Causal reasoning is a cornerstone of how humans interpret the world. To model and reason about causality, causal graphs offer a concise yet effective solution. Given the impressive advancements in language models, a crucial question arises: can they
Externí odkaz:
http://arxiv.org/abs/2406.16605
Autor:
Wang, Kun, Zhu, Jingya
In this study, we explore the characteristics of higgsino-dominated dark matter (DM) within the semi-constrained Next-to-Minimal Supersymmetric Standard Model (scNMSSM), covering a mass range from hundreds of GeV to several TeV. We carefully analyzed
Externí odkaz:
http://arxiv.org/abs/2406.15939
Autor:
Wang, Kun, Zhang, Guibin, Zhang, Xinnan, Fang, Junfeng, Wu, Xun, Li, Guohao, Pan, Shirui, Huang, Wei, Liang, Yuxuan
Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based learning tasks. Notably, most current GNN architectures operate under the assumption of homophily, whether explicitly or implicitly. While this underlying assumption is
Externí odkaz:
http://arxiv.org/abs/2406.12539