4 citations · 13 across the 6 of their papers we have counts for
6 papers
Near-Oracle Robustification of Finite-Difference Stochastic Gradient Estimators via Cheap Pilot Calibration
Haidong Li, Henry Lam, Yijie Peng
We study stochastic gradient estimation in black-box environments where only noisy simulation observations of function values are available. Finite-difference (FD) methods are amon…
One Forward is Enough for Neural Network Training via Likelihood Ratio Method
Jinyang Jiang, Zeliang Zhang, Chenliang Xu +2
While backpropagation (BP) is the mainstream approach for gradient computation in neural network training, its heavy reliance on the chain rule of differentiation constrains the de…
A Novel Noise Injection-based Training Scheme for Better Model Robustness
Zeliang Zhang, Jinyang Jiang, Minjie Chen +3
Noise injection-based method has been shown to be able to improve the robustness of artificial neural networks in previous work. In this work, we propose a novel noise injection-ba…
Pathological Image Segmentation with Noisy Labels
Li Xiao, Yinhao Li, Luxi Qv +3
Segmentation of pathological images is essential for accurate disease diagnosis. The quality of manual labels plays a critical role in segmentation accuracy; yet, in practice, the…
Noise Optimization for Artificial Neural Networks
Li Xiao, Zeliang Zhang, Yijie Peng
Adding noises to artificial neural network(ANN) has been shown to be able to improve robustness in previous work. In this work, we propose a new technique to compute the pathwise s…
Training Artificial Neural Networks by Generalized Likelihood Ratio Method: Exploring Brain-like Learning to Improve Robustness
Li Xiao, Yijie Peng, Jeff Hong +2
In this work, we propose a generalized likelihood ratio method capable of training the artificial neural networks with some biological brain-like mechanisms,.e.g., (a) learning by…