216 citations · 802 across the 61 of their papers we have counts for
5 papers · 1 filter
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…
Improving Adversarial Transferability with Scheduled Step Size and Dual Example
Zeliang Zhang, Peihan Liu, Xiaosen Wang +1
Deep neural networks are widely known to be vulnerable to adversarial examples, especially showing significantly poor performance on adversarial examples generated under the white-…
Cubic Spline Smoothing Compensation for Irregularly Sampled Sequences
Jing Shi, Jing Bi, Yingru Liu +1
The marriage of recurrent neural networks and neural ordinary differential networks (ODE-RNN) is effective in modeling irregularly-observed sequences. While ODE produces the smooth…
Explaining Local, Global, And Higher-Order Interactions In Deep Learning
Samuel Lerman, Chenliang Xu, Charles Venuto +1
We present a simple yet highly generalizable method for explaining interacting parts within a neural network's reasoning process. First, we design an algorithm based on cross deriv…
GAN-EM: GAN based EM learning framework
Wentian Zhao, Shaojie Wang, Zhihuai Xie +2
Expectation maximization (EM) algorithm is to find maximum likelihood solution for models having latent variables. A typical example is Gaussian Mixture Model (GMM) which requires…