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Found 6 papers, 2 papers with code
Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
We propose a novel paradigm that provides a unified framework of training neural operators and solving PDEs with the variational form, which we refer to as the variational operator learning (VOL).
Spatiotemporal Transformer Attention Network for 3D Voxel Level Joint Segmentation and Motion Prediction in Point Cloud
The current challenges of this solution are how to effectively combine different perception tasks into a single backbone and how to efficiently learn the spatiotemporal features directly from point cloud sequences.
Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections
However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system.
Attend and select: A segment selective transformer for microblog hashtag generation
These tokens or phrases may originate from primary fragmental textual pieces (e. g., segments) in the original text and are separated into different segments.
End-to-End Vision-Based Adaptive Cruise Control (ACC) Using Deep Reinforcement Learning
This paper presented a deep reinforcement learning method named Double Deep Q-networks to design an end-to-end vision-based adaptive cruise control (ACC) system.
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning.
