37 citations · 121 across the 28 of their papers we have counts for
5 papers · 1 filter
Spectral Signatures of Memorization Basins in Diffusion Trajectories
Achref Jaziri, Martin Mundt, Ahmed Imtiaz Humayun +1
Diffusion models are known to reproduce individual examples from their training data as a form of memorization with implications for generalization, privacy, and downstream use. Re…
Language Models Are "Insecure" Reporters
Jenny Y. Huang, Jiameng Fan, Ahmed Imtiaz Humayun +5
As large language models are deployed in increasingly autonomous long-horizon tasks, manually auditing and verifying the actions, artifacts, and outputs of models becomes more diff…
The Geometric Structure of Models Learning Sparse Data
Thomas Walker, T. Mitchell Roddenberry, Ahmed Imtiaz Humayun +2
The manifold hypothesis (MH) is often used to explain how machine learning can overcome the curse of dimensionality. However, the MH is only applicable in regimes where the trainin…
The Linear Centroids Hypothesis: Features as Directions Learned by Local Experts
Thomas Walker, Ahmed Imtiaz Humayun, Randall Balestriero +1
The Linear Representation Hypothesis (LRH) identifies features of a trained deep network (DN) as linear directions in the activation spaces, i.e., output spaces of intermediate lay…
We Should Distinguish Unlearning From Untraining
Eleni Triantafillou, Ahmed Imtiaz Humayun, Monica Ribero +3
There has been a recent surge in interest about the question of how we can ``delete'' specific data points or behaviours from a trained model, a goal referred to as ``machine unlea…