10 citations · 14 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Investigating task-specific prompts and sparse autoencoders for activation monitoring
Henk Tillman, Dan Mossing
Language models can behave in unexpected and unsafe ways, and so it is valuable to monitor their outputs. Internal activations of language models encode additional information that…
cs.LG2024★ 10 cited
Scaling and evaluating sparse autoencoders
Leo Gao, Tom Dupré la Tour, Henk Tillman +6
Sparse autoencoders provide a promising unsupervised approach for extracting interpretable features from a language model by reconstructing activations from a sparse bottleneck lay…
cs.CV2020★ 4 cited
SegNBDT: Visual Decision Rules for Segmentation
Alvin Wan, Daniel Ho, Younjin Song +3
The black-box nature of neural networks limits model decision interpretability, in particular for high-dimensional inputs in computer vision and for dense pixel prediction tasks li…