3 citations · 5 across the 12 of their papers we have counts for
13 papers
Revenge of Monosemanticity: Neuron Specialization as a New Form of Feature Learning in MLPs
Amirhesam Abedsoltan, Enric Boix-Adsera, Fivos Kalogiannis +1
Understanding how neural networks learn and organize features is central to understanding their behavior. Much existing theory of feature learning has focused on the emergence of a…
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…
A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis
Akash Kumar, Rahul Parhi, Mikhail Belkin
Recent works have characterized the function-space inductive bias of infinite-width bounded-norm single-hidden-layer neural networks as a kind of bounded-variation-type space. This…
Toward universal steering and monitoring of AI models
Daniel Beaglehole, Adityanarayanan Radhakrishnan, Enric Boix-Adserà +1
Modern AI models contain much of human knowledge, yet understanding of their internal representation of this knowledge remains elusive. Characterizing the structure and properties…
Fast training of large kernel models with delayed projections
Amirhesam Abedsoltan, Siyuan Ma, Parthe Pandit +1
Classical kernel machines have historically faced significant challenges in scaling to large datasets and model sizes--a key ingredient that has driven the success of neural networ…
Mirror Descent on Reproducing Kernel Banach Spaces
Akash Kumar, Mikhail Belkin, Parthe Pandit
Recent advances in machine learning have led to increased interest in reproducing kernel Banach spaces (RKBS) as a more general framework that extends beyond reproducing kernel Hil…
Context-Scaling versus Task-Scaling in In-Context Learning
Amirhesam Abedsoltan, Adityanarayanan Radhakrishnan, Jingfeng Wu +1
Transformers exhibit In-Context Learning (ICL), where these models solve new tasks by using examples in the prompt without additional training. In our work, we identify and analyze…
Eigenvectors of the De Bruijn Graph Laplacian: A Natural Basis for the Cut and Cycle Space
Anthony Philippakis, Neil Mallinar, Parthe Pandit +1
We study the Laplacian of the undirected De Bruijn graph over an alphabet of order . While the eigenvalues of this Laplacian were found in 1998 by Delorme and Tillich [1], a…
Emergence in non-neural models: grokking modular arithmetic via average gradient outer product
Neil Mallinar, Daniel Beaglehole, Libin Zhu +3
Neural networks trained to solve modular arithmetic tasks exhibit grokking, a phenomenon where the test accuracy starts improving long after the model achieves 100% training accura…
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…
On the Nystrom Approximation for Preconditioning in Kernel Machines
Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher +1
Kernel methods are a popular class of nonlinear predictive models in machine learning. Scalable algorithms for learning kernel models need to be iterative in nature, but convergenc…
Linear Recursive Feature Machines provably recover low-rank matrices
Adityanarayanan Radhakrishnan, Mikhail Belkin, Dmitriy Drusvyatskiy
A fundamental problem in machine learning is to understand how neural networks make accurate predictions, while seemingly bypassing the curse of dimensionality. A possible explanat…
Mechanism of feature learning in convolutional neural networks
Daniel Beaglehole, Adityanarayanan Radhakrishnan, Parthe Pandit +1
Understanding the mechanism of how convolutional neural networks learn features from image data is a fundamental problem in machine learning and computer vision. In this work, we i…