1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CL2024★ 1 cited
The Effect of Model Size on LLM Post-hoc Explainability via LIME
Henning Heyen, Amy Widdicombe, Noah Y. Siegel +2
Large language models (LLMs) are becoming bigger to boost performance. However, little is known about how explainability is affected by this trend. This work explores LIME explanat…
cs.CV2021
Gradient-Based Interpretability Methods and Binarized Neural Networks
Amy Widdicombe, Simon J. Julier
Binarized Neural Networks (BNNs) have the potential to revolutionize the way that deep learning is carried out in edge computing platforms. However, the effectiveness of interpreta…
cs.CV2019★ 1 cited
NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning
Moustafa Alzantot, Amy Widdicombe, Simon Julier +1
Deep Neural Networks (DNNs) deliver state-of-the-art performance in many image recognition and understanding applications. However, despite their outstanding performance, these mod…