83 citations · 141 across the 19 of their papers we have counts for
19 papers
An axiomatized PDE model of deep neural networks
Tangjun Wang, Wenqi Tao, Chenglong Bao +1
Inspired by the relation between deep neural network (DNN) and partial differential equations (PDEs), we study the general form of the PDE models of deep neural networks. To achiev…
Semi-Supervised Clustering via Dynamic Graph Structure Learning
Huaming Ling, Chenglong Bao, Xin Liang +1
Most existing semi-supervised graph-based clustering methods exploit the supervisory information by either refining the affinity matrix or directly constraining the low-dimensional…
Convergence Rates of Training Deep Neural Networks via Alternating Minimization Methods
Jintao Xu, Chenglong Bao, Wenxun Xing
Training deep neural networks (DNNs) is an important and challenging optimization problem in machine learning due to its non-convexity and non-separable structure. The alternating…
A scalable deep learning approach for solving high-dimensional dynamic optimal transport
Wei Wan, Yuejin Zhang, Chenglong Bao +2
The dynamic formulation of optimal transport has attracted growing interests in scientific computing and machine learning, and its computation requires to solve a PDE-constrained o…
Unsupervised Deep Learning Meets Chan-Vese Model
Dihan Zheng, Chenglong Bao, Zuoqiang Shi +2
The Chan-Vese (CV) model is a classic region-based method in image segmentation. However, its piecewise constant assumption does not always hold for practical applications. Many im…
Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach
Dihan Zheng, Xiaowen Zhang, Kaisheng Ma +1
Collecting paired training data is difficult in practice, but the unpaired samples broadly exist. Current approaches aim at generating synthesized training data from unpaired sampl…
AFEC: Active Forgetting of Negative Transfer in Continual Learning
Liyuan Wang, Mingtian Zhang, Zhongfan Jia +5
Continual learning aims to learn a sequence of tasks from dynamic data distributions. Without accessing to the old training samples, knowledge transfer from the old tasks to each n…
Diffusion Mechanism in Residual Neural Network: Theory and Applications
Tangjun Wang, Zehao Dou, Chenglong Bao +1
Diffusion, a fundamental internal mechanism emerging in many physical processes, describes the interaction among different objects. In many learning tasks with limited training sam…
A Semismooth Newton based Augmented Lagrangian Method for Nonsmooth Optimization on Matrix Manifolds
Yuhao Zhou, Chenglong Bao, Chao Ding +1
This paper is devoted to studying an augmented Lagrangian method for solving a class of manifold optimization problems, which have nonsmooth objective functions and nonlinear const…
Tightness and Equivalence of Semidefinite Relaxations for MIMO Detection
Ruichen Jiang, Ya-Feng Liu, Chenglong Bao +1
The multiple-input multiple-output (MIMO) detection problem, a fundamental problem in modern digital communications, is to detect a vector of transmitted symbols from the noisy out…
Interpolation between Residual and Non-Residual Networks
Zonghan Yang, Yang Liu, Chenglong Bao +1
Although ordinary differential equations (ODEs) provide insights for designing network architectures, its relationship with the non-residual convolutional neural networks (CNNs) is…
Efficient numerical methods for computing the stationary states of phase field crystal models
Kai Jiang, Wei Si, Chen Chang +1
Finding the stationary states of a free energy functional is an important problem in phase field crystal (PFC) models. Many efforts have been devoted for designing numerical scheme…
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
Shaokai Ye, Kailu Wu, Mu Zhou +6
Existing domain adaptation methods aim at learning features that can be generalized among domains. These methods commonly require to update source classifier to adapt to the target…
Exploring Frequency Domain Interpretation of Convolutional Neural Networks
Zhongfan Jia, Chenglong Bao, Kaisheng Ma
Many existing interpretation methods of convolutional neural networks (CNNs) mainly analyze in spatial domain, yet model interpretability in frequency domain has been rarely studie…
An efficient method for computing stationary states of phase field crystal models
Kai Jiang, Wei Si, Chenglong Bao
Computing stationary states is an important topic for phase field crystal (PFC) models. Great efforts have been made for energy dissipation of the numerical schemes when using grad…
Brain-inspired reverse adversarial examples
Shaokai Ye, Sia Huat Tan, Kaidi Xu +3
A human does not have to see all elephants to recognize an animal as an elephant. On contrast, current state-of-the-art deep learning approaches heavily depend on the variety of tr…
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models
Linfeng Zhang, Zhanhong Tan, Jiebo Song +3
Remarkable achievements have been attained by deep neural networks in various applications. However, the increasing depth and width of such models also lead to explosive growth in…
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation
Linfeng Zhang, Jiebo Song, Anni Gao +3
Convolutional neural networks have been widely deployed in various application scenarios. In order to extend the applications' boundaries to some accuracy-crucial domains, research…
Wasserstein-Fisher-Rao Document Distance
Zihao Wang, Datong Zhou, Yong Zhang +2
As a fundamental problem of natural language processing, it is important to measure the distance between different documents. Among the existing methods, the Word Mover's Distance…