Yupei Zhang,Xian Sheng,Mengfei Liu et al.
Yupei Zhang et al.
Graph transformers (GTs) have recently attracted considerable attention for graph representation learning (GRL). However, the existing methods often neglect hyper-order structures that arise from implicit node-groups within graphs. More cri...
Sliding Integral Neural Network Driven Robust Solution for Time-Varying Quadratic Programming [0.03%]
基于滑动积分神经网络的时间变化二次规划的鲁棒解法研究
Yang Si,Zhibin Li,Kai Zhao et al.
Yang Si et al.
Time-varying quadratic programming (TVQP) requires efficient, accurate, and robust online solvers. Existing discrete-time (DT) recurrent neural networks (RNNs), however, often face a tradeoff between solution precision and noise immunity. T...
Attention-Guided and Role-Aware Reinforcement Learning for Multi-AUV Counter-Game [0.03%]
基于注意力引导和角色感知的多AUV对抗博弈强化学习方法
Wenhao Gan,Kai Guo,Lei Qiao
Wenhao Gan
This article proposes an attention-guided, role-aware multiagent deep reinforcement learning (MADRL) scheme to enhance collaborative decision-making among autonomous underwater vehicles (AUVs) in the counter-game (CG). First, a customized m...
WiCount-DASL: Domain-Adversarial Semisupervised Learning for Wi-Fi-Based Stationary Crowd Counting [0.03%]
基于Wi-Fi的静态人群计数的领域对抗半监督学习方法WiFiCount-DASL
He Wang,Ivan Wang-Hei Ho
He Wang
Wi-Fi sensing provides a privacy-preserving and device-free sensing modality for stationary crowd counting with a low deployment cost. However, labeled channel state information (CSI) data are difficult to obtain at scale, and CSI distribut...
Unleashing the Potential of Imperfect Demonstration for Imitation Learning via Hierarchical Expert Guidance [0.03%]
基于分层专家指导的模仿学习的不完美示范潜能释放
Zhiliang Lin,Zhuangzhuang Chen,Guanming Zhu et al.
Zhiliang Lin et al.
Despite the substantial progress of imitation learning (IL) in training agents to mimic expert behavior, existing methods still suffer from covariate shift and compounding errors due to the limited availability of expert demonstrations and ...
Han Liu,Zhiliang Hao,Haoliang Ming et al.
Han Liu et al.
Graph long-tailed learning has garnered significant research attention. However, prevailing works in this domain typically assume the cleanliness of training dataset labels, neglecting the reality of noisy labels in real-world data. Such ch...
An Uncertainty-Aware Ensemble Approach to Modeling Utility of Pseudolabels for Semisupervised Learning [0.03%]
一种不确定性感知的集成方法，用于建模半监督学习中伪标签的效用
Jiaqi Wu,Junbiao Pang,Qingming Huang
Jiaqi Wu
Semisupervised learning (SSL) typically filters out low-confidence predictions when generating pseudolabels. This paradigm suffers from two critical limitations: 1) the lack of an effective strategy for determining a confidence threshold an...
Spatiotemporal Context-Aware Prompting With Low-Rank Dynamic Routing for Exemplar-Free Video Class-Incremental Learning [0.03%]
基于低秩动态路由的时空上下文感知提示的无示例视频类别渐进学习方法
Kunlun Wu,Bo Peng,Donghai Zhai
Kunlun Wu
Video class-incremental learning (VCIL) aims to progressively recognize novel action categories while preserving spatial-temporal knowledge of previous tasks. Unlike image class-incremental learning (CIL), VCIL requires simultaneously captu...
Structure-Guided Domain-Adaptive Network for Few-Shot SAR Ship Detection [0.03%]
基于结构引导的领域适应网络的少样本SAR船舶检测方法
Kang Ni,Weihang Zhou
Kang Ni
Due to the complexity of synthetic aperture radar (SAR) imaging mechanisms, SAR ship detection faces challenges such as difficulty in sample annotation and the influence of complex backgrounds, leading to poor target readability and difficu...
Interpreting the Observed Behavior of a Class of Autonomous Linear Systems Using Explainable Inverse Reinforcement Learning [0.03%]
基于可解释的逆强化学习的自主线性系统的观测行为解读
Adolfo Perrusquia,Mengbang Zou,Weisi Guo
Adolfo Perrusquia
One of the main challenges faced by society is how to verify the safety of autonomous systems. As the level of autonomy grows, it becomes critical to understand why an autonomous system exhibits a particular behavior and what we need to do ...