20 citations · 134 across the 33 of their papers we have counts for
8 papers · 1 filter
Fooling Adversarial Training with Inducing Noise
Zhirui Wang, Yifei Wang, Yisen Wang
Adversarial training is widely believed to be a reliable approach to improve model robustness against adversarial attack. However, in this paper, we show that when trained on one t…
Residual Relaxation for Multi-view Representation Learning
Yifei Wang, Zhengyang Geng, Feng Jiang +4
Multi-view methods learn representations by aligning multiple views of the same image and their performance largely depends on the choice of data augmentation. In this paper, we no…
The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program
Yifei Wang, Mert Pilanci
We study non-convex subgradient flows for training two-layer ReLU neural networks from a convex geometry and duality perspective. We characterize the implicit bias of unregularized…
Parallel Deep Neural Networks Have Zero Duality Gap
Yifei Wang, Tolga Ergen, Mert Pilanci
Training deep neural networks is a challenging non-convex optimization problem. Recent work has proven that the strong duality holds (which means zero duality gap) for regularized…
Reparameterized Sampling for Generative Adversarial Networks
Yifei Wang, Yisen Wang, Jiansheng Yang +1
Recently, sampling methods have been successfully applied to enhance the sample quality of Generative Adversarial Networks (GANs). However, in practice, they typically have poor sa…
Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality
Jonathan Lacotte, Yifei Wang, Mert Pilanci
We propose a randomized algorithm with quadratic convergence rate for convex optimization problems with a self-concordant, composite, strongly convex objective function. Our method…
Projected Wasserstein gradient descent for high-dimensional Bayesian inference
Yifei Wang, Peng Chen, Wuchen Li
We propose a projected Wasserstein gradient descent method (pWGD) for high-dimensional Bayesian inference problems. The underlying density function of a particle system of WGD is a…
Dissecting the Diffusion Process in Linear Graph Convolutional Networks
Yifei Wang, Yisen Wang, Jiansheng Yang +1
Graph Convolutional Networks (GCNs) have attracted more and more attentions in recent years. A typical GCN layer consists of a linear feature propagation step and a nonlinear trans…