2 citations · 3 across the 3 of their papers we have counts for
3 papers
math.OC2025
Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization
Aditya Ranganath, Mukesh Singhal, Roummel Marcia
Stochastic gradient descent and other first-order variants, such as Adam and AdaGrad, are commonly used in the field of deep learning due to their computational efficiency and low-…
cs.CV2024★ 2 cited
VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise
Zhixun He, Mukesh Singhal
Deep Neural Networks (DNN) have become a promising paradigm when developing Artificial Intelligence (AI) and Machine Learning (ML) applications. However, DNN applications are vulne…
cs.LG2021★ 1 cited
Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors
Pooya Tavallali, Vahid Behzadan, Peyman Tavallali +1
State-of-the-art machine learning models are vulnerable to data poisoning attacks whose purpose is to undermine the integrity of the model. However, the current literature on data…