Search Results for author:
Found 11 papers, 3 papers with code
Scientific Preparation for CSST: Classification of Galaxy and Nebula/Star Cluster Based on Deep Learning
Real-time identification of galaxy and nebula/star cluster (abbreviated as NSC) images is of great value during CSST survey.
Dynamically Conservative Self-Driving Planner for Long-Tail Cases
In this way, the DCP is designed to automatically adjust to be more conservative in low-confidence "long-tail" cases while keeping efficient otherwise.
Identify, Estimate and Bound the Uncertainty of Reinforcement Learning for Autonomous Driving
By constraining the uncertainty, the DRL model's performance is always greater than that of a baseline policy.
Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions
However, training AVs usually requires a large amount of training data collected from different driving environments (e. g., cities) as well as different types of personal information (e. g., working hours and routes).
Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles
To this end, this work proposes a trajectory planner to consider the prediction model uncertainty arising from insufficient data for safer performance.
Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward
These scenarios indeed correspond to the vulnerabilities of the under-test driving policies, thus are meaningful for their further improvements.
Sill-Net: Feature Augmentation with Separated Illumination Representation
For visual object recognition tasks, the illumination variations can cause distinct changes in object appearance and thus confuse the deep neural network based recognition models.
Ranked #1 on
Traffic Sign Recognition
on TopLogo-10
A Theory of Self-Supervised Framework for Few-Shot Learning
Recently, self-supervised learning (SSL) algorithms have been applied to Few-shot learning(FSL).
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning
This paper proposes a driving-policy adaptive safeguard (DPAS) design, including a collision avoidance strategy and an activation function.
Learning Fast Approximations of Sparse Nonlinear Regression
The idea of unfolding iterative algorithms as deep neural networks has been widely applied in solving sparse coding problems, providing both solid theoretical analysis in convergence rate and superior empirical performance.
Monocular Depth Prediction through Continuous 3D Loss
In contrast to the current point-to-point loss evaluation approach, the proposed 3D loss treats point clouds as continuous objects; therefore, it compensates for the lack of dense ground truth depth due to LIDAR's sparsity measurements.
