31 citations · 35 across the 6 of their papers we have counts for
6 papers
The Generalizability of Explanations
Hanxiao Tan
Due to the absence of ground truth, objective evaluation of explainability methods is an essential research direction. So far, the vast majority of evaluations can be summarized in…
Maximum Entropy Baseline for Integrated Gradients
Hanxiao Tan
Integrated Gradients (IG), one of the most popular explainability methods available, still remains ambiguous in the selection of baseline, which may seriously impair the credibilit…
Visualizing Global Explanations of Point Cloud DNNs
Hanxiao Tan
In the field of autonomous driving and robotics, point clouds are showing their excellent real-time performance as raw data from most of the mainstream 3D sensors. Therefore, point…
Explainability-Aware One Point Attack for Point Cloud Neural Networks
Hanxiao Tan, Helena Kotthaus
With the proposition of neural networks for point clouds, deep learning has started to shine in the field of 3D object recognition while researchers have shown an increased interes…
Surrogate Model-Based Explainability Methods for Point Cloud NNs
Hanxiao Tan, Helena Kotthaus
In the field of autonomous driving and robotics, point clouds are showing their excellent real-time performance as raw data from most of the mainstream 3D sensors. Therefore, point…
Explainable Machine Learning with Prior Knowledge: An Overview
Katharina Beckh, Sebastian Müller, Matthias Jakobs +6
This survey presents an overview of integrating prior knowledge into machine learning systems in order to improve explainability. The complexity of machine learning models has elic…