222 citations · 338 across the 23 of their papers we have counts for
31 papers
DEMAND: Deep Matrix Approximately Nonlinear Decomposition to Identify Meta, Canonical, and Sub-Spatial Pattern of functional Magnetic Resonance Imaging in the Human Brain
Wei Zhang, Yu Bao
Deep Neural Networks (DNNs) have already become a crucial computational approach to revealing the spatial patterns in the human brain; however, there are three major shortcomings i…
SADAM: Stochastic Adam, A Stochastic Operator for First-Order Gradient-based Optimizer
Wei Zhang, Yu Bao
In this work, to efficiently help escape the stationary and saddle points, we propose, analyze, and generalize a stochastic strategy performed as an operator for a first-order grad…
Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations
Na Liu, Mark Dras, Wei Emma Zhang
Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbatio…
Beam Training and Alignment for RIS-Assisted Millimeter Wave Systems:State of the Art and Beyond
Peilan Wang, Jun Fang, Weizheng Zhang +3
Reconfigurable intelligent surface (RIS) has recently emerged as a promising paradigm for future cellular networks. Specifically, due to its capability in reshaping the propagation…
Adaptive Multi-Teacher Multi-level Knowledge Distillation
Yuang Liu, Wei Zhang, Jun Wang
Knowledge distillation~(KD) is an effective learning paradigm for improving the performance of lightweight student networks by utilizing additional supervision knowledge distilled…
Adam: A Stochastic Method with Adaptive Variance Reduction
Mingrui Liu, Wei Zhang, Francesco Orabona +1
Adam is a widely used stochastic optimization method for deep learning applications. While practitioners prefer Adam because it requires less parameter tuning, its use is problemat…