4 citations · 4 across the 2 of their papers we have counts for
3 papers
A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs
Devansh Bisla, Apoorva Nandini Saridena, Anna Choromanska
This paper focuses on understanding how the generalization error scales with the amount of the training data for deep neural networks (DNNs). Existing techniques in statistical lea…
Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy
Naman Patel, Apoorva Nandini Saridena, Anna Choromanska +2
The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle…
LSALSA: Accelerated Source Separation via Learned Sparse Coding
Benjamin Cowen, Apoorva Nandini Saridena, Anna Choromanska
We propose an efficient algorithm for the generalized sparse coding (SC) inference problem. The proposed framework applies to both the single dictionary setting, where each data po…