1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.AI2024★ 1 cited
Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience
Zhonghao He, Jascha Achterberg, Katie Collins +13
As deep learning systems are scaled up to many billions of parameters, relating their internal structure to external behaviors becomes very challenging. Although daunting, this pro…
cs.LG2024
The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability
Stephen Casper, Jieun Yun, Joonhyuk Baek +13
Interpretability techniques are valuable for helping humans understand and oversee AI systems. The SaTML 2024 CNN Interpretability Competition solicited novel methods for studying…
cs.CL2024★ 1 cited
Eight Methods to Evaluate Robust Unlearning in LLMs
Aengus Lynch, Phillip Guo, Aidan Ewart +2
Machine unlearning can be useful for removing harmful capabilities and memorized text from large language models (LLMs), but there are not yet standardized methods for rigorously e…