Temporal-Aware Bidirectional Local Label Propagation for Semi-Supervised Time Series Forecasting Under Sparse Supervision [0.03%]
基于时间感知的双向局部标签传播的半监督时空序列预测方法
Suting Gao,Wen Yu,Tianyou Chai
Suting Gao
The rapid growth of time-series data collection has made large-scale observations readily available, yet labeled annotations remain inherently sparse due to practical and domain-specific constraints, such as costly, time-consuming, and expe...
Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey With Perspectives [0.03%]
解决车辆路径问题的神经组合优化算法：带有观点的全面调查
Xuan Wu,Lijie Wen,Yubin Xiao et al.
Xuan Wu et al.
Although several surveys on neural combinatorial optimization (NCO) solvers specifically designed to solve vehicle routing problems (VRPs) have been conducted, they did not cover the state-of-the-art (SOTA) NCO solvers emerged recently. Mor...
Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects [0.03%]
自主驾驶的思考链条：全面调查和未来展望
Yixin Cui,Haotian Lin,Shuo Yang et al.
Yixin Cui et al.
The rapid evolution of large language models (LLMs) in natural language processing has substantially elevated their semantic understanding and logical reasoning capabilities. Such proficiencies have been leveraged in autonomous driving syst...
Less Repetition, Less Energy Cost: A Reinforcement Learning-Based Multiagent Energy-Saving Autonomous Exploration System [0.03%]
减少重复，降低能耗：一种基于强化学习的多智能体节能自主探索系统
Yang Liu,Peng Zhang,Yanting Li et al.
Yang Liu et al.
Multiagent autonomous exploration in unknown environments is both meaningful and challenging. Due to the constraint of a partially observable environment, the collaboration among agents is often inadequate, leading to increased energy consu...
Permutation-Invariant Quantum Graph Neural Network Based on Variational Quantum Algorithms [0.03%]
基于变分量子算法的置换不变量子图神经网络
Maoduo Li,Wen Liu,Lei Shi et al.
Maoduo Li et al.
Graph neural networks (GNNs) have demonstrated strong capabilities in graph representation learning but still face limitations in efficiency and scalability. Quantum GNNs (QGNNs) offer a promising alternative. However, existing approaches o...
Robust Ensemble Learning Under Label Noise: A Theoretical Analysis and Framework-Specific Solutions [0.03%]
标签噪声下的鲁棒集成学习：一种理论分析和框架特定的解决方案
Guanxiong He,Jie Wang,Zhiyong Li et al.
Guanxiong He et al.
Ensemble learning methods combine multiple weak base learners to create a robust decision model, effectively analyzing feature-response relationships across various domains. However, the assumption of accurate sample-label relationships in ...
Cloud Server Replenishment Policy Under Given Demand Satisfaction Rates via Reinforcement Learning With Policy Imitation [0.03%]
基于策略模仿的 reinforcement learning 在给定需求满足率下的云服务器补给政策
Jingze Li,Pengzhi Cheng,Xinyu Zhang et al.
Jingze Li et al.
Cloud service providers face the challenge of determining optimal server replenishment policies that minimize inventory costs while ensuring the expected demand satisfaction rate. This article addresses a long-term, single-echelon inventory...
Multipatch Augmentation Learning Based on Dual-Policy Model for Time Series Classification [0.03%]
基于双策略模型的多补丁增强学习时间序列分类方法
Chentao Liu,Xin Huo,Changchun He et al.
Chentao Liu et al.
Multiple instance learning (MIL)-based time series classification (TSC) predicts instance-level labels and aggregates them to derive bag-level labels; however, this approach encounters instability as anomalous instances significantly impact...
AHRL-PM: Asynchronous Hierarchical Reinforcement Learning Framework for Enhanced Portfolio Management [0.03%]
异步分层强化学习框架在增强型组合管理中的应用 AHRL-PM
Shuyue Liu,Tianxiang Cui,Yiran Li et al.
Shuyue Liu et al.
Effective portfolio management (PM) is a cornerstone of financial strategy, yet it is often challenged by the uncertainties and the high dimensionality of market data. Traditional PM techniques, whether model-based or model-free, frequently...
Cheng Zhou,Xin Man,Congshan Ma et al.
Cheng Zhou et al.
This article addresses the multinode cooperative jamming problem in communication networks with unknown topology. To overcome the combinatorial explosion in decision-making and the credit assignment challenge under full-bandit feedback, we ...