133 citations · 157 across the 8 of their papers we have counts for
9 papers
An Illusion of Unlearning? Assessing Machine Unlearning Through Internal Representations
Yichen Gao, Altay Unal, Akshay Rangamani +1
While numerous machine unlearning (MU) methods have recently been developed with promising results in erasing the influence of forgotten data, classes, or concepts, they are also h…
Deep Neural Regression Collapse
Akshay Rangamani, Altay Unal
Neural Collapse is a phenomenon that helps identify sparse and low rank structures in deep classifiers. Recent work has extended the definition of neural collapse to regression pro…
Low Rank and Sparse Fourier Structure in Recurrent Networks Trained on Modular Addition
Akshay Rangamani
Modular addition tasks serve as a useful test bed for observing empirical phenomena in deep learning, including the phenomenon of \emph{grokking}. Prior work has shown that one-lay…
On Generalization Bounds for Neural Networks with Low Rank Layers
Andrea Pinto, Akshay Rangamani, Tomaso Poggio
While previous optimization results have suggested that deep neural networks tend to favour low-rank weight matrices, the implications of this inductive bias on generalization boun…
Neural-guided, Bidirectional Program Search for Abstraction and Reasoning
Simon Alford, Anshula Gandhi, Akshay Rangamani +6
One of the challenges facing artificial intelligence research today is designing systems capable of utilizing systematic reasoning to generalize to new tasks. The Abstraction and R…
For interpolating kernel machines, minimizing the norm of the ERM solution minimizes stability
Akshay Rangamani, Lorenzo Rosasco, Tomaso Poggio
We study the average $\mbox{CV}_{loo}$ stability of kernel ridge-less regression and derive corresponding risk bounds. We show that the interpolating solution with minimum norm min…
A Scale Invariant Flatness Measure for Deep Network Minima
Akshay Rangamani, Nam H. Nguyen, Abhishek Kumar +3
It has been empirically observed that the flatness of minima obtained from training deep networks seems to correlate with better generalization. However, for deep networks with pos…
Automated software vulnerability detection with machine learning
Jacob A. Harer, Louis Y. Kim, Rebecca L. Russell +13
Thousands of security vulnerabilities are discovered in production software each year, either reported publicly to the Common Vulnerabilities and Exposures database or discovered i…
Sparse Coding and Autoencoders
Akshay Rangamani, Anirbit Mukherjee, Amitabh Basu +4
In "Dictionary Learning" one tries to recover incoherent matrices (typically overcomplete and whose columns are assumed to be normalized) and spar…