Search Results for author: Zenglin Xu

Found 93 papers, 34 papers with code

Creating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: Sindhi

no code implementations EACL (WASSA) 2021 Wazir Ali, Naveed Ali, Yong Dai, Jay Kumar, Saifullah Tumrani, Zenglin Xu

In this paper, we develop Sindhi subjective lexicon using a merger of existing English resources: NRC lexicon, list of opinion words, SentiWordNet, Sindhi-English bilingual dictionary, and collection of Sindhi modifiers.

Sentiment Analysis Subjectivity Analysis +1

SiPOS: A Benchmark Dataset for Sindhi Part-of-Speech Tagging

no code implementations RANLP 2021 Wazir Ali, Zenglin Xu, Jay Kumar

In this paper, we introduce the SiPOS dataset for part-of-speech tagging in the low-resource Sindhi language with quality baselines.

Part-Of-Speech Tagging text annotation

Ti-MAE: Self-Supervised Masked Time Series Autoencoders

no code implementations21 Jan 2023 Zhe Li, Zhongwen Rao, Lujia Pan, Pengyun Wang, Zenglin Xu

Multivariate Time Series forecasting has been an increasingly popular topic in various applications and scenarios.

Contrastive Learning Multivariate Time Series Forecasting +1

When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods

1 code implementation20 Dec 2022 Zhuo Zhang, Yuanhang Yang, Yong Dai, Lizhen Qu, Zenglin Xu

To facilitate the research of PETuning in FL, we also develop a federated tuning framework FedPETuning, which allows practitioners to exploit different PETuning methods under the FL training paradigm conveniently.

Federated Learning

MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completion

no code implementations19 Dec 2022 Zi Gong, Yinpeng Guo, Pingyi Zhou, Cuiyun Gao, Yasheng Wang, Zenglin Xu

On the other hand, there are few studies exploring the effects of multi-programming-lingual (MultiPL) pre-training for the code completion, especially the impact on low-resource programming languages.

Code Completion

Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid

1 code implementation30 Oct 2022 Jing Xu, Xu Luo, Xinglin Pan, Wenjie Pei, Yanan Li, Zenglin Xu

In this paper, we find that this problem usually occurs when the positions of support samples are in the vicinity of task centroid -- the mean of all class centroids in the task.

Few-Shot Learning Selection bias

Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling

no code implementations11 Oct 2022 Yuanhang Yang, shiyi qi, Cuiyun Gao, Zenglin Xu, Yulan He, Qifan Wang, Chuanyi Liu

Transformer-based models have achieved great success on sentence pair modeling tasks, such as answer selection and natural language inference (NLI).

Answer Selection Natural Language Inference +1

ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization

no code implementations16 Jun 2022 Langzhang Liang, Zenglin Xu, Zixing Song, Irwin King, Jieping Ye

In detail, by studying the long-tailed distribution of node degrees in the graph, we propose a novel normalization method for GNNs, which is termed ResNorm (\textbf{Res}haping the long-tailed distribution into a normal-like distribution via \textbf{norm}alization).

Node Classification

Channel Importance Matters in Few-Shot Image Classification

1 code implementation16 Jun 2022 Xu Luo, Jing Xu, Zenglin Xu

When facing novel few-shot tasks in the test-time datasets, this transformation can greatly improve the generalization ability of learned image representations, while being agnostic to the choice of training algorithms and datasets.

Few-Shot Image Classification

A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks

1 code implementation28 May 2022 Yu Pan, Zeyong Su, Ao Liu, Jingquan Wang, Nannan Li, Zenglin Xu

To address this problem, we propose a universal weight initialization paradigm, which generalizes Xavier and Kaiming methods and can be widely applicable to arbitrary TCNNs.

Tensor Decomposition

Aggregating Gradients in Encoded Domain for Federated Learning

no code implementations26 May 2022 Dun Zeng, Shiyu Liu, Siqi Liang, Zonghang Li, Zenglin Xu

Malicious attackers and an honest-but-curious server can steal private client data from uploaded gradients in federated learning.

Federated Learning

Contrastive Multi-view Hyperbolic Hierarchical Clustering

no code implementations5 May 2022 Fangfei Lin, Bing Bai, Kun Bai, Yazhou Ren, Peng Zhao, Zenglin Xu

Then, we embed the representations into a hyperbolic space and optimize the hyperbolic embeddings via a continuous relaxation of hierarchical clustering loss.

Semantically Proportional Patchmix for Few-Shot Learning

no code implementations17 Feb 2022 Jingquan Wang, Jing Xu, Yu Pan, Zenglin Xu

Few-shot learning aims to classify unseen classes with only a limited number of labeled data.

Few-Shot Learning Transfer Learning

Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT

1 code implementation3 Feb 2022 Zonghang Li, Yihong He, Hongfang Yu, Jiawen Kang, Xiaoping Li, Zenglin Xu, Dusit Niyato

In this paper, we propose FedGS, which is a hierarchical cloud-edge-end FL framework for 5G empowered industries, to improve industrial FL performance on non-i. i. d.

Federated Learning

Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training

no code implementations31 Jan 2022 Shenglai Zeng, Zonghang Li, Hongfang Yu, Yihong He, Zenglin Xu, Dusit Niyato, Han Yu

In this paper, we propose a data heterogeneity-robust FL approach, FedGSP, to address this challenge by leveraging on a novel concept of dynamic Sequential-to-Parallel (STP) collaborative training.

Federated Learning Privacy Preserving

Exploring Category-correlated Feature for Few-shot Image Classification

no code implementations14 Dec 2021 Jing Xu, Xinglin Pan, Xu Luo, Wenjie Pei, Zenglin Xu

To alleviate this problem, we present a simple yet effective feature rectification method by exploring the category correlation between novel and base classes as the prior knowledge.

Classification Few-Shot Image Classification

Self-Paced Deep Regression Forests with Consideration of Ranking Fairness

1 code implementation13 Dec 2021 Lili Pan, Mingming Meng, Yazhou Ren, Yali Zheng, Zenglin Xu

To answer this question, this paper proposes a new SPL method: easy and underrepresented examples first, for learning DDMs.

Age Estimation Fairness +3

Graph Partner Neural Networks for Semi-Supervised Learning on Graphs

no code implementations18 Oct 2021 Langzhang Liang, Cuiyun Gao, Shiyi Chen, Shishi Duan, Yu Pan, Junjin Zheng, Lei Wang, Zenglin Xu

Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, link prediction, and graph classification.

Classification Graph Classification +2

FedLab: A Flexible Federated Learning Framework

1 code implementation24 Jul 2021 Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu

Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations.

Federated Learning

ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE

1 code implementation NeurIPS 2021 Qingzhong Ai, Lirong He, Shiyu Liu, Zenglin Xu

To address this issue, we propose Bayesian Pseudocoresets Exemplar VAE (ByPE-VAE), a new variant of VAE with a prior based on Bayesian pseudocoreset.

Data Augmentation Density Estimation +2

Boosting Few-Shot Classification with View-Learnable Contrastive Learning

1 code implementation20 Jul 2021 Xu Luo, Yuxuan Chen, Liangjian Wen, Lili Pan, Zenglin Xu

The goal of few-shot classification is to classify new categories with few labeled examples within each class.

Classification Contrastive Learning +1

Stein Variational Gradient Descent with Multiple Kernel

no code implementations20 Jul 2021 Qingzhong Ai, Shiyu Liu, Lirong He, Zenglin Xu

In practice, we notice that the kernel used in SVGD-based methods has a decisive effect on the empirical performance.

Discrete Auto-regressive Variational Attention Models for Text Modeling

1 code implementation16 Jun 2021 Xianghong Fang, Haoli Bai, Jian Li, Zenglin Xu, Michael Lyu, Irwin King

We further design discrete latent space for the variational attention and mathematically show that our model is free from posterior collapse.

Language Modelling

AFINet: Attentive Feature Integration Networks for Image Classification

no code implementations10 May 2021 Xinglin Pan, Jing Xu, Yu Pan, Liangjian Wen, WenXiang Lin, Kun Bai, Zenglin Xu

Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification.

Classification General Classification +1

Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge

no code implementations9 May 2021 Yong Dai, Jian Liu, Jian Zhang, Hongguang Fu, Zenglin Xu

The first mechanism is a selective domain adaptation (SDA) method, which transfers knowledge from the closest source domain.

Domain Adaptation Sentiment Analysis

TedNet: A Pytorch Toolkit for Tensor Decomposition Networks

1 code implementation11 Apr 2021 Yu Pan, Maolin Wang, Zenglin Xu

Tensor Decomposition Networks (TDNs) prevail for their inherent compact architectures.

Tensor Decomposition

Pseudo-supervised Deep Subspace Clustering

1 code implementation8 Apr 2021 Juncheng Lv, Zhao Kang, Xiao Lu, Zenglin Xu

To tackle these problems, we use pairwise similarity to weigh the reconstruction loss to capture local structure information, while a similarity is learned by the self-expression layer.

Partial Differential Equations is All You Need for Generating Neural Architectures -- A Theory for Physical Artificial Intelligence Systems

no code implementations10 Mar 2021 Ping Guo, Kaizhu Huang, Zenglin Xu

In this work, we generalize the reaction-diffusion equation in statistical physics, Schr\"odinger equation in quantum mechanics, Helmholtz equation in paraxial optics into the neural partial differential equations (NPDE), which can be considered as the fundamental equations in the field of artificial intelligence research.

Contrastive Disentanglement in Generative Adversarial Networks

no code implementations5 Mar 2021 Lili Pan, Peijun Tang, Zhiyong Chen, Zenglin Xu

Disentanglement is defined as the problem of learninga representation that can separate the distinct, informativefactors of variations of data.

Contrastive Learning Disentanglement

A Survey on Deep Semi-supervised Learning

no code implementations28 Feb 2021 Xiangli Yang, Zixing Song, Irwin King, Zenglin Xu

Deep semi-supervised learning is a fast-growing field with a range of practical applications.

Graph-based Semi-supervised Learning: A Comprehensive Review

1 code implementation26 Feb 2021 Zixing Song, Xiangli Yang, Zenglin Xu, Irwin King

An important class of SSL methods is to naturally represent data as graphs such that the label information of unlabelled samples can be inferred from the graphs, which corresponds to graph-based semi-supervised learning (GSSL) methods.

Graph Embedding

MultiFace: A Generic Training Mechanism for Boosting Face Recognition Performance

1 code implementation25 Jan 2021 Jing Xu, Tszhang Guo, Yong Xu, Zenglin Xu, Kun Bai

Deep Convolutional Neural Networks (DCNNs) and their variants have been widely used in large scale face recognition(FR) recently.

Face Recognition

AFINets: Attentive Feature Integration Networks for Image Classification

no code implementations1 Jan 2021 Xinglin Pan, Jing Xu, Yu Pan, WenXiang Lin, Liangjian Wen, Zenglin Xu

Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks, e. g., image classification.

Classification General Classification +1

Block-term Tensor Neural Networks

no code implementations10 Oct 2020 Jinmian Ye, Guangxi Li, Di Chen, Haiqin Yang, Shandian Zhe, Zenglin Xu

Deep neural networks (DNNs) have achieved outstanding performance in a wide range of applications, e. g., image classification, natural language processing, etc.

Image Classification

Heuristic Rank Selection with Progressively Searching Tensor Ring Network

no code implementations22 Sep 2020 Nannan Li, Yu Pan, Yaran Chen, Zixiang Ding, Dongbin Zhao, Zenglin Xu

Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region.

Structured Graph Learning for Clustering and Semi-supervised Classification

no code implementations31 Aug 2020 Zhao Kang, Chong Peng, Qiang Cheng, Xinwang Liu, Xi Peng, Zenglin Xu, Ling Tian

Furthermore, most existing graph-based methods conduct clustering and semi-supervised classification on the graph learned from the original data matrix, which doesn't have explicit cluster structure, thus they might not achieve the optimal performance.

Classification General Classification +1

Deep Embedded Multi-view Clustering with Collaborative Training

1 code implementation26 Jul 2020 Jie Xu, Yazhou Ren, Guofeng Li, Lili Pan, Ce Zhu, Zenglin Xu

Firstly, the embedded representations of multiple views are learned individually by deep autoencoders.

CoreGen: Contextualized Code Representation Learning for Commit Message Generation

1 code implementation14 Jul 2020 Lun Yiu Nie, Cuiyun Gao, Zhicong Zhong, Wai Lam, Yang Liu, Zenglin Xu

In this paper, we propose a novel Contextualized code representation learning strategy for commit message Generation (CoreGen).

Representation Learning Text Generation

Relation-Guided Representation Learning

no code implementations11 Jul 2020 Zhao Kang, Xiao Lu, Jian Liang, Kun Bai, Zenglin Xu

In this work, we propose a new representation learning method that explicitly models and leverages sample relations, which in turn is used as supervision to guide the representation learning.

Representation Learning

Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis

no code implementations10 Jun 2020 Yong Dai, Jian Liu, Xiancong Ren, Zenglin Xu

Existing algorithms of MS-UDA either only exploit the shared features, i. e., the domain-invariant information, or based on some weak assumption in NLP, e. g., smoothness assumption.

Multi-Source Unsupervised Domain Adaptation Sentiment Analysis +1

Angular Triplet Loss-based Camera Network for ReID

no code implementations12 May 2020 Yitian Li, Ruini Xue, Mengmeng Zhu, Jing Xu, Zenglin Xu

Many complex network structures are proposed recently and many of them concentrate on multi-branch features to achieve high performance.

Person Re-Identification Retrieval

Mutual Information Gradient Estimation for Representation Learning

1 code implementation ICLR 2020 Liangjian Wen, Yiji Zhou, Lirong He, Mingyuan Zhou, Zenglin Xu

To this end, we propose the Mutual Information Gradient Estimator (MIGE) for representation learning based on the score estimation of implicit distributions.

Representation Learning

SiNER: A Large Dataset for Sindhi Named Entity Recognition

no code implementations LREC 2020 Wazir Ali, Junyu Lu, Zenglin Xu

We introduce the SiNER: a named entity recognition (NER) dataset for low-resourced Sindhi language with quality baselines.

named-entity-recognition NER

Discrete Variational Attention Models for Language Generation

no code implementations21 Apr 2020 Xianghong Fang, Haoli Bai, Zenglin Xu, Michael Lyu, Irwin King

Variational autoencoders have been widely applied for natural language generation, however, there are two long-standing problems: information under-representation and posterior collapse.

Language Modelling Text Generation

Self-Paced Deep Regression Forests with Consideration on Underrepresented Examples

no code implementations ECCV 2020 Lili Pan, Shijie Ai, Yazhou Ren, Zenglin Xu

Deep discriminative models (e. g. deep regression forests, deep neural decision forests) have achieved remarkable success recently to solve problems such as facial age estimation and head pose estimation.

Age Estimation Fairness +2

Structure Learning with Similarity Preserving

no code implementations3 Dec 2019 Zhao Kang, Xiao Lu, Yiwei Lu, Chong Peng, Zenglin Xu

Leveraging on the underlying low-dimensional structure of data, low-rank and sparse modeling approaches have achieved great success in a wide range of applications.

Multi-view Subspace Clustering via Partition Fusion

no code implementations3 Dec 2019 Juncheng Lv, Zhao Kang, Boyu Wang, Luping Ji, Zenglin Xu

Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way.

Graph Learning Multi-view Subspace Clustering

Word Embedding based New Corpus for Low-resourced Language: Sindhi

no code implementations28 Nov 2019 Wazir Ali, Jay Kumar, Junyu Lu, Zenglin Xu

Our intrinsic evaluation results demonstrate the high quality of our generated Sindhi word embeddings using SG, CBoW, and GloVe as compare to SdfastText word representations.

Word Embeddings

Large-scale Multi-view Subspace Clustering in Linear Time

2 code implementations21 Nov 2019 Zhao Kang, Wangtao Zhou, Zhitong Zhao, Junming Shao, Meng Han, Zenglin Xu

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years.

Multi-view Subspace Clustering

Deep Minimax Probability Machine

no code implementations20 Nov 2019 Lirong He, Ziyi Guo, Kai-Zhu Huang, Zenglin Xu

In a worst-case scenario, MPM tries to minimize an upper bound of misclassification probabilities, considering the global information (i. e., mean and covariance information of each class).

On Model Robustness Against Adversarial Examples

no code implementations15 Nov 2019 Shufei Zhang, Kai-Zhu Huang, Zenglin Xu

We propose to exploit an energy function to describe the stability and prove that reducing such energy guarantees the robustness against adversarial examples.

Multi-graph Fusion for Multi-view Spectral Clustering

1 code implementation16 Sep 2019 Zhao Kang, Guoxin Shi, Shudong Huang, Wenyu Chen, Xiaorong Pu, Joey Tianyi Zhou, Zenglin Xu

Most existing methods don't pay attention to the quality of the graphs and perform graph learning and spectral clustering separately.

Graph Learning

Multiple Partitions Aligned Clustering

1 code implementation13 Sep 2019 Zhao Kang, Zipeng Guo, Shudong Huang, Siying Wang, Wenyu Chen, Yuanzhang Su, Zenglin Xu

Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie.

Learning to Search Efficient DenseNet with Layer-wise Pruning

no code implementations ICLR 2019 Xuanyang Zhang, Hao liu, Zhanxing Zhu, Zenglin Xu

Deep neural networks have achieved outstanding performance in many real-world applications with the expense of huge computational resources.

Low-rank Kernel Learning for Graph-based Clustering

no code implementations14 Mar 2019 Zhao Kang, Liangjian Wen, Wenyu Chen, Zenglin Xu

By formulating graph construction and kernel learning in a unified framework, the graph and consensus kernel can be iteratively enhanced by each other.

graph construction Graph Learning

Similarity Learning via Kernel Preserving Embedding

1 code implementation11 Mar 2019 Zhao Kang, Yiwei Lu, Yuanzhang Su, Changsheng Li, Zenglin Xu

Data similarity is a key concept in many data-driven applications.

DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification

no code implementations30 Dec 2018 Xianghong Fang, Haoli Bai, Ziyi Guo, Bin Shen, Steven Hoi, Zenglin Xu

In this paper, we propose a new unsupervised domain adaptation method named Domain-Adversarial Residual-Transfer (DART) learning of Deep Neural Networks to tackle cross-domain image classification tasks.

Classification General Classification +2

Robust Graph Learning from Noisy Data

1 code implementation17 Dec 2018 Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu

The proposed model is able to boost the performance of data clustering, semisupervised classification, and data recovery significantly, primarily due to two key factors: 1) enhanced low-rank recovery by exploiting the graph smoothness assumption, 2) improved graph construction by exploiting clean data recovered by robust PCA.

General Classification graph construction +6

Latent Dirichlet Allocation in Generative Adversarial Networks

no code implementations17 Dec 2018 Lili Pan, Shen Cheng, Jian Liu, Yazhou Ren, Zenglin Xu

We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs).

Image Generation multimodal generation +1

Deep Density-based Image Clustering

1 code implementation11 Dec 2018 Yazhou Ren, Ni Wang, Mingxia Li, Zenglin Xu

Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications.

Deep Clustering Image Clustering

Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition

1 code implementation NIPS Workshop CDNNRIA 2018 Yu Pan, Jing Xu, Maolin Wang, Jinmian Ye, Fei Wang, Kun Bai, Zenglin Xu

Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling.

Action Recognition Tensor Decomposition

Self-Paced Multi-Task Clustering

1 code implementation24 Aug 2018 Yazhou Ren, Xiaofan Que, Dezhong Yao, Zenglin Xu

Despite the success of traditional MTC models, they are either easy to stuck into local optima, or sensitive to outliers and noisy data.

Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification

no code implementations20 Jun 2018 Zhao Kang, Xiao Lu, Jin-Feng Yi, Zenglin Xu

There are two possible reasons for the failure: (i) most existing MKL methods assume that the optimal kernel is a linear combination of base kernels, which may not hold true; and (ii) some kernel weights are inappropriately assigned due to noises and carelessly designed algorithms.

General Classification

Adversarial Noise Layer: Regularize Neural Network By Adding Noise

1 code implementation21 May 2018 Zhonghui You, Jinmian Ye, Kunming Li, Zenglin Xu, Ping Wang

In this paper, we introduce a novel regularization method called Adversarial Noise Layer (ANL) and its efficient version called Class Adversarial Noise Layer (CANL), which are able to significantly improve CNN's generalization ability by adding carefully crafted noise into the intermediate layer activations.

SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks

no code implementations13 Jan 2018 Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska

Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance.

Management Scheduling

BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition

no code implementations15 Dec 2017 Guangxi Li, Jinmian Ye, Haiqin Yang, Di Chen, Shuicheng Yan, Zenglin Xu

Recently, deep neural networks (DNNs) have been regarded as the state-of-the-art classification methods in a wide range of applications, especially in image classification.

General Classification Image Classification

Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition

no code implementations CVPR 2018 Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu

On three challenging tasks, including Action Recognition in Videos, Image Captioning and Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of both prediction accuracy and convergence rate.

Action Recognition Action Recognition In Videos +4

Two Birds with One Stone: Transforming and Generating Facial Images with Iterative GAN

no code implementations16 Nov 2017 Dan Ma, Bin Liu, Zhao Kang, Jiayu Zhou, Jianke Zhu, Zenglin Xu

Generating high fidelity identity-preserving faces with different facial attributes has a wide range of applications.

Image Generation

Unified Spectral Clustering with Optimal Graph

1 code implementation12 Nov 2017 Zhao Kang, Chong Peng, Qiang Cheng, Zenglin Xu

Second, the discrete solution may deviate from the spectral solution since k-means method is well-known as sensitive to the initialization of cluster centers.

graph construction

Stochastic Sequential Neural Networks with Structured Inference

no code implementations24 May 2017 Hao Liu, Haoli Bai, Lirong He, Zenglin Xu

Inheriting these advantages of stochastic neural sequential models, we propose a structured and stochastic sequential neural network, which models both the long-term dependencies via recurrent neural networks and the uncertainty in the segmentation and labels via discrete random variables.

Medical Diagnosis Time Series

Simple and Efficient Parallelization for Probabilistic Temporal Tensor Factorization

no code implementations11 Nov 2016 Guangxi Li, Zenglin Xu, Linnan Wang, Jinmian Ye, Irwin King, Michael Lyu

Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data.

Tensor Decomposition via Variational Auto-Encoder

no code implementations3 Nov 2016 Bin Liu, Zenglin Xu, Yingming Li

Another assumption of these methods is that a predefined rank should be known.

Tensor Decomposition

Exact and Stable Recovery of Pairwise Interaction Tensors

no code implementations NeurIPS 2013 Shouyuan Chen, Michael R. Lyu, Irwin King, Zenglin Xu

For the noisy cases, we also prove error bounds for a constrained convex program for recovering the tensors.

Collaborative Filtering

Supervised Heterogeneous Multiview Learning for Joint Association Study and Disease Diagnosis

no code implementations26 Apr 2013 Shandian Zhe, Zenglin Xu, Yuan Qi

To unify these two tasks, we present a new sparse Bayesian approach for joint association study and disease diagnosis.

Association Multiview Learning

Robust Metric Learning by Smooth Optimization

no code implementations15 Mar 2012 Kaizhu Huang, Rong Jin, Zenglin Xu, Cheng-Lin Liu

Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints.

Combinatorial Optimization Metric Learning

Adaptive Regularization for Transductive Support Vector Machine

no code implementations NeurIPS 2009 Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu, Zhirong Yang

In this framework, SVM and TSVM can be regarded as a learning machine without regularization and one with full regularization from the unlabeled data, respectively.

Heavy-Tailed Symmetric Stochastic Neighbor Embedding

no code implementations NeurIPS 2009 Zhirong Yang, Irwin King, Zenglin Xu, Erkki Oja

Based on this finding, we present a parameterized subset of similarity functions for choosing the best tail-heaviness for HSSNE; (2) we present a fixed-point optimization algorithm that can be applied to all heavy-tailed functions and does not require the user to set any parameters; and (3) we present two empirical studies, one for unsupervised visualization showing that our optimization algorithm runs as fast and as good as the best known t-SNE implementation and the other for semi-supervised visualization showing quantitative superiority using the homogeneity measure as well as qualitative advantage in cluster separation over t-SNE.

Data Visualization

An Extended Level Method for Efficient Multiple Kernel Learning

no code implementations NeurIPS 2008 Zenglin Xu, Rong Jin, Irwin King, Michael Lyu

We consider the problem of multiple kernel learning (MKL), which can be formulated as a convex-concave problem.

Efficient Convex Relaxation for Transductive Support Vector Machine

no code implementations NeurIPS 2007 Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu

We consider the problem of Support Vector Machine transduction, which involves a combinatorial problem with exponential computational complexity in the number of unlabeled examples.

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