Search Results for author: Xiaofeng Zhang

Found 22 papers, 7 papers with code

Building the Directed Semantic Graph for Coherent Long Text Generation

no code implementations EMNLP 2021 Ziao Wang, Xiaofeng Zhang, Hongwei Du

These directed subgraphs are considered to well preserve extra but relevant content to the short input text, and then they are decoded by the employed pre-trained model to generate coherent long text.

Sentence Embedding Sentence-Embedding +1

Transformer-Patcher: One Mistake worth One Neuron

1 code implementation24 Jan 2023 Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie zhou, Wenge Rong, Zhang Xiong

Our method outperforms previous fine-tuning and HyperNetwork-based methods and achieves state-of-the-art performance for Sequential Model Editing (SME).

Mixture of Attention Heads: Selecting Attention Heads Per Token

1 code implementation11 Oct 2022 Xiaofeng Zhang, Yikang Shen, Zeyu Huang, Jie zhou, Wenge Rong, Zhang Xiong

This paper proposes the Mixture of Attention Heads (MoA), a new architecture that combines multi-head attention with the MoE mechanism.

Language Modelling Machine Translation +1

GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs

no code implementations12 Aug 2022 Linhao Luo, Yixiang Fang, Moli Lu, Xin Cao, Xiaofeng Zhang, Wenjie Zhang

Most of existing relevance measures focus on homogeneous networks where objects are of the same type, and a few measures are developed for heterogeneous graphs, but they often need the pre-defined meta-path.

Community Detection Graph Mining +1

VDPC: Variational Density Peak Clustering Algorithm

no code implementations29 Dec 2021 Yizhang Wang, Di Wang, You Zhou, Xiaofeng Zhang, Chai Quek

Furthermore, we divide all data points into different levels according to their local density and propose a unified clustering framework by combining the advantages of both DPC and DBSCAN.

DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation

no code implementations30 May 2021 Liqi Yang, Linhan Luo, Lifeng Xin, Xiaofeng Zhang, Xinni Zhang

Then, the demand-aware graph neural network is designed to extract session demand graph to learn the demand-aware item embedddings for the later recommendations.

Session-Based Recommendations

RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating

no code implementations25 Nov 2020 Linhao Luo, Liqi Yang, Ju Xin, Yixiang Fang, Xiaofeng Zhang, Xiaofei Yang, Kai Chen, Zhiyuan Zhang, Kai Liu

In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation.

Developing Univariate Neurodegeneration Biomarkers with Low-Rank and Sparse Subspace Decomposition

no code implementations26 Oct 2020 Gang Wang, Qunxi Dong, Jianfeng Wu, Yi Su, Kewei Chen, Qingtang Su, Xiaofeng Zhang, Jinguang Hao, Tao Yao, Li Liu, Caiming Zhang, Richard J Caselli, Eric M Reiman, Yalin Wang

With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25$\%$ reduction in the mean annual change with 80$\%$ power and two-tailed $P=0. 05$ are 116, 279 and 387 for the longitudinal $A\beta+$ AD, $A\beta+$ mild cognitive impairment (MCI) and $A\beta+$ CU groups, respectively.

Generating Long Financial Report using Conditional Variational Autoencoders with Knowledge Distillation

no code implementations23 Oct 2020 Yunpeng Ren, Ziao Wang, Yiyuan Wang, Xiaofeng Zhang

Particularly, we choose Bi-GRU as the encoder and decoder component of CVAE, and learn the latent variable distribution from input news.

Knowledge Distillation

SiENet: Siamese Expansion Network for Image Extrapolation

1 code implementation8 Jul 2020 Xiaofeng Zhang, Feng Chen, Cailing Wang, Songsong Wu, Ming Tao, Guoping Jiang

In this paper, a novel two-stage siamese adversarial model for image extrapolation, named Siamese Expansion Network (SiENet) is proposed.

Image Inpainting Image Outpainting

Automatically Generating Macro Research Reports from a Piece of News

no code implementations21 Nov 2019 Wenxin Hu, Xiaofeng Zhang, Gang Yang

As we all know, it requires the macro analysts to write such reports within a short period of time after the important economic news are released.

Text Generation

Structure Matters: Towards Generating Transferable Adversarial Images

no code implementations22 Oct 2019 Dan Peng, Zizhan Zheng, Linhao Luo, Xiaofeng Zhang

In this paper, we propose the novel concepts of structure patterns and structure-aware perturbations that relax the small perturbation constraint while still keeping images natural.

Image Classification Novel Concepts

Heterogeneous-Temporal Graph Convolutional Networks: Make the Community Detection Much Better

no code implementations23 Sep 2019 Yaping Zheng, Shiyi Chen, Xinni Zhang, Xiaofeng Zhang, Xiaofei Yang, Di Wang

Community detection has long been an important yet challenging task to analyze complex networks with a focus on detecting topological structures of graph data.

Community Detection

Wasserstein Autoencoders for Collaborative Filtering

no code implementations15 Sep 2018 Jingbin Zhong, Xiaofeng Zhang

Two different cost functions are designed for measuring the distance between the implicit feedback data and its re-generated version of data.

Collaborative Filtering Recommendation Systems

Structure-Preserving Transformation: Generating Diverse and Transferable Adversarial Examples

1 code implementation8 Sep 2018 Dan Peng, Zizhan Zheng, Xiaofeng Zhang

A common requirement in all these works is that the malicious perturbations should be small enough (measured by an L_p norm for some p) so that they are imperceptible to humans.

Image Classification

DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification

2 code implementations29 Aug 2018 Xiaofeng Zhang, Zhangyang Wang, Dong Liu, Qing Ling

Given insufficient data, while many techniques have been developed to help combat overfitting, the challenge remains if one tries to train deep networks, especially in the ill-posed extremely low data regimes: only a small set of labeled data are available, and nothing -- including unlabeled data -- else.

Data Augmentation General Classification +1

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