Search Results for author:
Found 7 papers, 3 papers with code
Describe-and-Dissect: Interpreting Neurons in Vision Networks with Language Models
In this paper, we propose Describe-and-Dissect (DnD), a novel method to describe the roles of hidden neurons in vision networks.
Corrupting Neuron Explanations of Deep Visual Features
The inability of DNNs to explain their black-box behavior has led to a recent surge of explainability methods.
The Importance of Prompt Tuning for Automated Neuron Explanations
Recent advances have greatly increased the capabilities of large language models (LLMs), but our understanding of the models and their safety has not progressed as fast.
Concept-Monitor: Understanding DNN training through individual neurons
In this work, we propose a general framework called Concept-Monitor to help demystify the black-box DNN training processes automatically using a novel unified embedding space and concept diversity metric.
Label-Free Concept Bottleneck Models
Motivated by these challenges, we propose Label-free CBM which is a novel framework to transform any neural network into an interpretable CBM without labeled concept data, while retaining a high accuracy.
CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks
Finally CLIP-Dissect is computationally efficient and can label all neurons from five layers of ResNet-50 in just 4 minutes, which is more than 10 times faster than existing methods.
Robust Deep Reinforcement Learning through Adversarial Loss
To address this issue, we propose RADIAL-RL, a principled framework to train reinforcement learning agents with improved robustness against $l_p$-norm bounded adversarial attacks.
