3 citations · 7 across the 15 of their papers we have counts for
6 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…
Steering Autoregressive Music Generation with Recursive Feature Machines
Daniel Zhao, Daniel Beaglehole, Taylor Berg-Kirkpatrick +2
Controllable music generation remains a significant challenge, with existing methods often requiring model retraining or introducing audible artifacts. We introduce MusicRFM, a fra…
xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
Daniel Beaglehole, David Holzmüller, Adityanarayanan Radhakrishnan +1
Inference from tabular data, collections of continuous and categorical variables organized into matrices, is a foundation for modern technology and science. Yet, in contrast to the…
Average gradient outer product as a mechanism for deep neural collapse
Daniel Beaglehole, Peter Súkeník, Marco Mondelli +1
Deep Neural Collapse (DNC) refers to the surprisingly rigid structure of the data representations in the final layers of Deep Neural Networks (DNNs). Though the phenomenon has been…
Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features
Adityanarayanan Radhakrishnan, Daniel Beaglehole, Parthe Pandit +1
In recent years neural networks have achieved impressive results on many technological and scientific tasks. Yet, the mechanism through which these models automatically select feat…
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions
Daniel Beaglehole, Mikhail Belkin, Parthe Pandit
``Benign overfitting'', the ability of certain algorithms to interpolate noisy training data and yet perform well out-of-sample, has been a topic of considerable recent interest. W…