69 citations · 217 across the 26 of their papers we have counts for
4 papers · 1 filter
Exact Dynamics and Finite-Sample Trajectory Recovery of Linear Recursive Feature Machines
Andrew Cheng, Bobak T. Kiani, Yue M. Lu +1
Recursive feature machines (RFMs) learn representations of data by alternating between fitting a predictor to a dataset and updating features of that predictor using the average gr…
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…
Efficient and accurate steering of Large Language Models through attention-guided feature learning
Parmida Davarmanesh, Ashia Wilson, Adityanarayanan Radhakrishnan
Steering, or direct manipulation of internal activations to guide LLM responses toward specific semantic concepts, is emerging as a promising avenue for both understanding how sema…