3 citations · 7 across the 15 of their papers we have counts for
3 papers · 1 filter
The Weight Gram Matrix Captures Sequential Feature Linearization in Deep Networks
Taehun Cha, Daniel Beaglehole, Adityanarayanan Radhakrishnan +1
Understanding how deep neural networks learn representations remains a central challenge in machine learning theory. In this work, we propose a feature-centric framework for analyz…
Contextual Linear Activation Steering of Language Models
Brandon Hsu, Daniel Beaglehole, Adityanarayanan Radhakrishnan +1
Linear activation steering is a powerful approach for eliciting the capabilities of large language models and specializing their behavior using limited labeled data. While effectiv…
DANCE: Doubly Adaptive Neighborhood Conformal Estimation
Brandon R. Feng, Brian J. Reich, Daniel Beaglehole +7
The recent developments of complex deep learning models have led to unprecedented ability to accurately predict across multiple data representation types. Conformal prediction for…