27 citations · 49 across the 9 of their papers we have counts for
6 papers · 1 filter
Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning
Bao Wang, Stanley J. Osher
Improving the accuracy and robustness of deep neural nets (DNNs) and adapting them to small training data are primary tasks in deep learning research. In this paper, we replace the…
A semi-implicit relaxed Douglas-Rachford algorithm (sir-DR) for Ptychograhpy
Minh Pham, Arjun Rana, Jianwei Miao +1
Alternating projection based methods, such as ePIE and rPIE, have been used widely in ptychography. However, they only work well if there are adequate measurements (diffraction pat…
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM
Bao Wang, Quanquan Gu, March Boedihardjo +2
Machine learning (ML) models trained by differentially private stochastic gradient descent (DP-SGD) have much lower utility than the non-private ones. To mitigate this degradation,…
Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets
Penghang Yin, Jiancheng Lyu, Shuai Zhang +3
Training activation quantized neural networks involves minimizing a piecewise constant function whose gradient vanishes almost everywhere, which is undesirable for the standard bac…
Unnormalized Optimal Transport
Wilfrid Gangbo, Wuchen Li, Stanley Osher +1
We propose an extension of the computational fluid mechanics approach to the Monge-Kantorovich mass transfer problem, which was developed by Benamou-Brenier. Our extension allows o…
A Deterministic Gradient-Based Approach to Avoid Saddle Points
Lisa Maria Kreusser, Stanley J. Osher, Bao Wang
Loss functions with a large number of saddle points are one of the major obstacles for training modern machine learning models efficiently. First-order methods such as gradient des…