Author name cluster

Changying Du

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7 papers

2 author rows

AAAI Conference 2020 Conference Paper

Conditional Generative Neural Decoding with Structured CNN Feature Prediction

Changde Du
Changying Du
Lijie Huang
Huiguang He

Decoding visual contents from human brain activity is a challenging task with great scientiﬁc value. Two main facts that hinder existing methods from producing satisfactory results are 1) typically small paired training data; 2) underexploitation of the structural information underlying the data. In this paper, we present a novel conditional deep generative neural decoding approach with structured intermediate feature prediction. Speciﬁcally, our approach ﬁrst decodes the brain activity to the multilayer intermediate features of a pretrained convolutional neural network (CNN) with a structured multioutput regression (SMR) model, and then inverts the decoded CNN features to the visual images with an introspective conditional generation (ICG) model. The proposed SMR model can simultaneously leverage the covariance structures underlying the brain activities, the CNN features and the prediction tasks to improve the decoding accuracy and interpretability. Further, our ICG model can 1) leverage abundant unpaired images to augment the training data; 2) self-evaluate the quality of its conditionally generated images; and 3) adversarially improve itself without extra discriminator. Experimental results show that our approach yields state-of-the-art visual reconstructions from brain activities.

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IJCAI Conference 2018 Conference Paper

Redundancy-resistant Generative Hashing for Image Retrieval

Changying Du
Xingyu Xie
Changde Du
Hao Wang

By optimizing probability distributions over discrete latent codes, Stochastic Generative Hashing (SGH) bypasses the critical and intractable binary constraints on hash codes. While encouraging results were reported, SGH still suffers from the deficient usage of latent codes, i. e. , there often exist many uninformative latent dimensions in the code space, a disadvantage inherited from its auto-encoding variational framework. Motivated by the fact that code redundancy usually is severer when more complex decoder network is used, in this paper, we propose a constrained deep generative architecture to simplify the decoder for data reconstruction. Specifically, our new framework forces the latent hashing codes to not only reconstruct data through the generative network but also retain minimal squared L2 difference to the last real-valued network hidden layer. Furthermore, during posterior inference, we propose to regularize the standard auto-encoding objective with an additional term that explicitly accounts for the negative redundancy degree of latent code dimensions. We interpret such modifications as Bayesian posterior regularization and design an adversarial strategy to optimize the generative, the variational, and the redundancy-resistanting parameters. Empirical results show that our new method can significantly boost the quality of learned codes and achieve state-of-the-art performance for image retrieval.

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IJCAI Conference 2017 Conference Paper

Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel

Jia He
Changying Du
Changde Du
Fuzhen Zhuang
Qing He
Guoping Long

Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models seek point estimation of their parameters, and are prone to overfitting to small training data. This paper presents an adaptive kernel nonlinear max-margin multi-view learning model under the Bayesian framework. Specifically, we regularize the posterior of an efficient multi-view latent variable model by explicitly mapping the latent representations extracted from multiple data views to a random Fourier feature space where max-margin classification constraints are imposed. Assuming these random features are drawn from Dirichlet process Gaussian mixtures, we can adaptively learn shift-invariant kernels from data according to Bochners theorem. For inference, we employ the data augmentation idea for hinge loss, and design an efficient gradient-based MCMC sampler in the augmented space. Having no need to compute the Gram matrix, our algorithm scales linearly with the size of training set. Extensive experiments on real-world datasets demonstrate that our method has superior performance.

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IJCAI Conference 2016 Conference Paper

Online Bayesian Max-Margin Subspace Multi-View Learning

Jia He
Changying Du
Fuzhen Zhuang
Xin Yin
Qing He
Guoping Long

Last decades have witnessed a number of studies devoted to multi-view learning algorithms, however, few efforts have been made to handle online multi-view learning scenarios. In this paper, we propose an online Bayesian multi-view learning algorithm to learn predictive subspace with max-margin principle. Specifically, we first define the latent margin loss for classification in the subspace, and then cast the learning problem into a variational Bayesian framework by exploiting the pseudo-likelihood and data augmentation idea. With the variational approximate posterior inferred from the past samples, we can naturally combine historical knowledge with new arrival data, in a Bayesian Passive-Aggressive style. Experiments on various classification tasks show that our model have superior performance.

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UAI Conference 2016 Conference Paper

Online Bayesian Multiple Kernel Bipartite Ranking

Changying Du
Changde Du
Guoping Long
Qing He 0003
Yucheng Li 0002

Bipartite ranking aims to maximize the area under the ROC curve (AUC) of a decision function. To tackle this problem when the data appears sequentially, existing online AUC maximization methods focus on seeking a point estimate of the decision function in a linear or predefined single kernel space, and cannot learn effective kernels automatically from the streaming data. In this paper, we first develop a Bayesian multiple kernel bipartite ranking model, which circumvents the kernel selection problem by estimating a posterior distribution over the model weights. To make our model applicable to streaming data, we then present a kernelized online Bayesian passive-aggressive learning framework by maintaining a variational approximation to the posterior based on data augmentation. Furthermore, to efficiently deal with large-scale data, we design a fixed budget strategy which can effectively control online model complexity. Extensive experimental studies confirm the superiority of our Bayesian multi-kernel approach.

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AAAI Conference 2015 Conference Paper

Bayesian Maximum Margin Principal Component Analysis

Changying Du
Shandian Zhe
Fuzhen Zhuang
Yuan Qi
Qing He
Zhongzhi Shi

Supervised dimensionality reduction has shown great advantages in finding predictive subspaces. Previous methods rarely consider the popular maximum margin principle and are prone to overfitting to usually small training data, especially for those under the maximum likelihood framework. In this paper, we present a posterior-regularized Bayesian approach to combine Principal Component Analysis (PCA) with the maxmargin learning. Based on the data augmentation idea for max-margin learning and the probabilistic interpretation of PCA, our method can automatically infer the weight and penalty parameter of max-margin learning machine, while finding the most appropriate PCA subspace simultaneously under the Bayesian framework. We develop a fast mean-field variational inference algorithm to approximate the posterior. Experimental results on various classification tasks show that our method outperforms a number of competitors.

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ECAI Conference 2014 Conference Paper

Nonparametric Bayesian Multi-Task Large-margin Classification

Changying Du
Jia He 0001
Fuzhen Zhuang
Yuan (Alan) Qi
Qing He 0003

In this paper, we present a nonparametric Bayesian multi-task large-margin classification model which can cluster tasks into the most appropriate number of groups and induce flexible model sharing within each task group simultaneously. Specifically, we first show a very simple method to integrate large margin learning with hierarchical Bayesian models by employing an important variant of the standard SVMi. e. proximal SVM (PSVM)whose loss function is used to define a novel likelihood function. And then we assume that the model parameter of each task consists of two parts: one is shared within each task group (group-level parameter) while the other is specific to each distinct task (task rescaling parameter). A Dirichlet process prior is imposed on the group-level parameter while the task rescaling parameter is assigned a one-mean Laplace prior. Finally the parameter of a task is the corresponding group parameter times its specific rescaling parameter. We give efficient Markov chain Monte Calo (MCMC) algorithm to conduct model inference. Experiments on the Landmine detection data and the UCI Yeast data demonstrate the effectiveness of our method.

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