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Found 11 papers, 1 papers with code
Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?
Transformer-based trajectory optimization methods have demonstrated exceptional performance in offline Reinforcement Learning (offline RL), yet it poses challenges due to substantial parameter size and limited scalability, which is particularly critical in sequential decision-making scenarios where resources are constrained such as in robots and drones with limited computational power.
Conversational Crowdsensing: A Parallel Intelligence Powered Novel Sensing Approach
The transition from CPS-based Industry 4. 0 to CPSS-based Industry 5. 0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in Chatbots and Large Language Models (LLMs).
Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation
To the best of our knowledge, this is the first systematic review of ML-related methods for BPP.
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction
In particular, to fully exploit the periodic information, we also improve the intensity function calculation of the point process with a periodic gated mechanism.
The Expressive Power of Graph Neural Networks: A Survey
Graph neural networks (GNNs) are effective machine learning models for many graph-related applications.
Large-scale Generative Simulation Artificial Intelligence: the Next Hotspot in Generative AI
The concept of GenAI has been developed for decades.
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods.
Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction
To overcome these limitations, we propose an automated dilated spatio-temporal synchronous graph network, named Auto-DSTSGN for traffic prediction.
Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation
To address the above problems, a novel graph-based deep learning framework for travel time estimation is proposed in this paper, namely Spatio-Temporal Dual Graph Neural Networks (STDGNN).
Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction
Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction.
VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control
Recent breakthroughs in Go play and strategic games have witnessed the great potential of reinforcement learning in intelligently scheduling in uncertain environment, but some bottlenecks are also encountered when we generalize this paradigm to universal complex tasks.
