4 papers · 1 filter
Can VLMs Reason Robustly? A Neuro-Symbolic Investigation
Weixin Chen, Antonio Vergari, Han Zhao
Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts. In this paper…
Causal Neural Probabilistic Circuits
Weixin Chen, Han Zhao
Concept Bottleneck Models (CBMs) enhance the interpretability of end-to-end neural networks by introducing a layer of concepts and predicting the class label from the concept predi…
Understanding and Improving Adversarial Robustness of Neural Probabilistic Circuits
Weixin Chen, Han Zhao
Neural Probabilistic Circuits (NPCs), a new class of concept bottleneck models, comprise an attribute recognition model and a probabilistic circuit for reasoning. By integrating th…
Neural Probabilistic Circuits: Enabling Compositional and Interpretable Predictions through Logical Reasoning
Weixin Chen, Simon Yu, Huajie Shao +2
End-to-end deep neural networks have achieved remarkable success across various domains but are often criticized for their lack of interpretability. While post hoc explanation meth…