56 citations · 283 across the 30 of their papers we have counts for
5 papers · 1 filter
Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum
Bao Wang, Qiang Ye
Momentum plays a crucial role in stochastic gradient-based optimization algorithms for accelerating or improving training deep neural networks (DNNs). In deep learning practice, th…
An Integrated Approach to Produce Robust Models with High Efficiency
Zhijian Li, Bao Wang, Jack Xin
Deep Neural Networks (DNNs) needs to be both efficient and robust for practical uses. Quantization and structure simplification are promising ways to adapt DNNs to mobile devices,…
Differentially Private Federated Learning with Laplacian Smoothing
Zhicong Liang, Bao Wang, Quanquan Gu +2
Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. However, an adversary may still be able to infer the p…
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets
Thu Dinh, Bao Wang, Andrea L. Bertozzi +1
Deep neural nets (DNNs) compression is crucial for adaptation to mobile devices. Though many successful algorithms exist to compress naturally trained DNNs, developing efficient an…
Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent
Bao Wang, Tan M. Nguyen, Andrea L. Bertozzi +2
Stochastic gradient descent (SGD) with constant momentum and its variants such as Adam are the optimization algorithms of choice for training deep neural networks (DNNs). Since DNN…