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Found 8 papers, 2 papers with code
Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain
This work provides a comprehensive analysis and interpretation of the graph spectral representation of traffic scenarios.
Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications
For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding.
Gradient Derivation for Learnable Parameters in Graph Attention Networks
This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs).
Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders
This constitutes a statistical inference task in terms of the optimal latent space activations of the underlying uncorrupted datum.
Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding
We present Center3D, a one-stage anchor-free approach, to efficiently estimate 3D location and depth using only monocular RGB images.
A2D2: Audi Autonomous Driving Dataset
Research in machine learning, mobile robotics, and autonomous driving is accelerated by the availability of high quality annotated data.
Stochastic determination of matrix determinants
Matrix determinants play an important role in data analysis, in particular when Gaussian processes are involved.
Data Analysis, Statistics and Probability Instrumentation and Methods for Astrophysics Computation Methodology
Signal inference with unknown response: Calibration-uncertainty renormalized estimator
The calibration of a measurement device is crucial for every scientific experiment, where a signal has to be inferred from data.
