5 papers
The Safety Challenge of World Models for Embodied AI Agents: A Review
Lorenzo Baraldi, Zifan Zeng, Chongzhe Zhang +8
The rapid progress in embodied artificial intelligence has highlighted the necessity for more advanced and integrated models that can perceive, interpret, and predict environmental…
Revisiting Data Attribution for Influence Functions
Hongbo Zhu, Angelo Cangelosi
The goal of data attribution is to trace the model's predictions through the learning algorithm and back to its training data. thereby identifying the most influential training sam…
Representation Understanding via Activation Maximization
Hongbo Zhu, Angelo Cangelosi
Understanding internal feature representations of deep neural networks (DNNs) is a fundamental step toward model interpretability. Inspired by neuroscience methods that probe biolo…
Noise-Free Explanation for Driving Action Prediction
Hongbo Zhu, Theodor Wulff, Rahul Singh Maharjan +2
Although attention mechanisms have achieved considerable progress in Transformer-based architectures across various Artificial Intelligence (AI) domains, their inner workings remai…
LIPEx-Locally Interpretable Probabilistic Explanations-To Look Beyond The True Class
Hongbo Zhu, Angelo Cangelosi, Procheta Sen +1
In this work, we instantiate a novel perturbation-based multi-class explanation framework, LIPEx (Locally Interpretable Probabilistic Explanation). We demonstrate that LIPEx not on…