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For interpolating kernel machines, the minimum norm ERM solution is the most stable

Jun 28, 2020
Akshay Rangamani, Lorenzo Rosasco, Tomaso Poggio

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A Scale Invariant Flatness Measure for Deep Network Minima

Feb 06, 2019
Akshay Rangamani, Nam H. Nguyen, Abhishek Kumar, Dzung Phan, Sang H. Chin, Trac D. Tran

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Automated software vulnerability detection with machine learning

Aug 02, 2018
Jacob A. Harer, Louis Y. Kim, Rebecca L. Russell, Onur Ozdemir, Leonard R. Kosta, Akshay Rangamani, Lei H. Hamilton, Gabriel I. Centeno, Jonathan R. Key, Paul M. Ellingwood, Erik Antelman, Alan Mackay, Marc W. McConley, Jeffrey M. Opper, Peter Chin, Tomo Lazovich

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Sparse Coding and Autoencoders

Oct 20, 2017
Akshay Rangamani, Anirbit Mukherjee, Amitabh Basu, Tejaswini Ganapathy, Ashish Arora, Sang Chin, Trac D. Tran

* In this new version of the paper with a small change in the distributional assumptions we are actually able to prove the asymptotic criticality of a neighbourhood of the ground truth dictionary for even just the standard squared loss of the ReLU autoencoder (unlike the regularized loss in the older version) 

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