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Found 5 papers, 4 papers with code
FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving
Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving.
A Deep Learning Technique using a Sequence of Follow Up X-Rays for Disease classification
In this paper, we have also established that without additional layers before the output classification, the CNN models will improve the performance of predicting the disease labels for each patient.
Interpretable Compositional Convolutional Neural Networks
The reasonable definition of semantic interpretability presents the core challenge in explainable AI.
3D-Rotation-Equivariant Quaternion Neural Networks
This paper proposes a set of rules to revise various neural networks for 3D point cloud processing to rotation-equivariant quaternion neural networks (REQNNs).
Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing
In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different intermediate-layer network architectures.
