Search Results for author: Xiaolong Li

Found 32 papers, 3 papers with code

Denoising User-aware Memory Network for Recommendation

no code implementations12 Jul 2021 Zhi Bian, Shaojun Zhou, Hao Fu, Qihong Yang, Zhenqi Sun, Junjie Tang, Guiquan Liu, Kaikui Liu, Xiaolong Li

Specifically, the framework: (i) proposes a feature purification module based on orthogonal mapping, which use the representation of explicit feedback to purify the representation of implicit feedback, and effectively denoise the implicit feedback; (ii) designs a user memory network to model the long-term interests in a fine-grained way by improving the memory network, which is ignored by the existing methods; and (iii) develops a preference-aware interactive representation component to fuse the long-term and short-term interests of users based on gating to understand the evolution of unbiased preferences of users.

Denoising

Super exponential divergence of periodic points for C^1-generic partially hyperbolic homoclinic classes

no code implementations1 Feb 2021 Xiaolong Li, Katsutoshi Shinohara

A diffeomorphism f is called super exponential divergent if for every r>1, the lower limit of #Per_n(f)/r^n diverges to infinity as n tends to infinity, where Per_n(f) is the set of all periodic points of f with period n. This property is stronger than the usual super exponential growth of the number of periodic points.

Dynamical Systems 37C20, 37C25, 37C29, 37D30

Interactive Question Clarification in Dialogue via Reinforcement Learning

no code implementations COLING 2020 Xiang Hu, Zujie Wen, Yafang Wang, Xiaolong Li, Gerard de Melo

In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query.

LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding

no code implementations14 Dec 2020 Hao Fu, Shaojun Zhou, Qihong Yang, Junjie Tang, Guiquan Liu, Kaikui Liu, Xiaolong Li

In this work, we propose a knowledge distillation method LRC-BERT based on contrastive learning to fit the output of the intermediate layer from the angular distance aspect, which is not considered by the existing distillation methods.

Contrastive Learning Knowledge Distillation +1

Handling Rare Entities for Neural Sequence Labeling

no code implementations ACL 2020 Yangming Li, Han Li, Kaisheng Yao, Xiaolong Li

One great challenge in neural sequence labeling is the data sparsity problem for rare entity words and phrases.

Unpack Local Model Interpretation for GBDT

no code implementations3 Apr 2020 Wenjing Fang, Jun Zhou, Xiaolong Li, Kenny Q. Zhu

Besides the commonly used feature importance as a global interpretation, feature contribution is a local measure that reveals the relationship between a specific instance and the related output.

Feature Importance

NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay

no code implementations1 Apr 2020 Jianbin Lin, Zhiqiang Zhang, Jun Zhou, Xiaolong Li, Jingli Fang, Yanming Fang, Quan Yu, Yuan Qi

Considering the above challenges and the special scenario in Ant Financial, we try to incorporate default prediction with network information to alleviate the cold-start problem.

Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization

no code implementations12 Mar 2020 Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li

However, existing MF approaches suffer from two major problems: (1) Expensive computations and storages due to the centralized model training mechanism: the centralized learners have to maintain the whole user-item rating matrix, and potentially huge low rank matrices.

Local Contextual Attention with Hierarchical Structure for Dialogue Act Recognition

no code implementations12 Mar 2020 Zhigang Dai, Jinhua Fu, Qile Zhu, Hengbin Cui, Xiaolong Li, Yuan Qi

We revise the attention distribution to focus on the local and contextual semantic information by incorporating the relative position information between utterances.

Hierarchical structure

RNE: A Scalable Network Embedding for Billion-scale Recommendation

no code implementations10 Mar 2020 Jianbin Lin, Daixin Wang, Lu Guan, Yin Zhao, Binqiang Zhao, Jun Zhou, Xiaolong Li, Yuan Qi

However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods.

Network Embedding

Long Short-Term Sample Distillation

no code implementations2 Mar 2020 Liang Jiang, Zujie Wen, Zhongping Liang, Yafang Wang, Gerard de Melo, Zhe Li, Liangzhuang Ma, Jiaxing Zhang, Xiaolong Li, Yuan Qi

The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher--student differences, while the short-term one yields more up-to-date cues with the goal of enabling higher-quality updates.

How Much Can A Retailer Sell? Sales Forecasting on Tmall

no code implementations27 Feb 2020 Chaochao Chen, Ziqi Liu, Jun Zhou, Xiaolong Li, Yuan Qi, Yujing Jiao, Xingyu Zhong

By analyzing the data, we have two main observations, i. e., sales seasonality after we group different groups of retails and a Tweedie distribution after we transform the sales (target to forecast).

Time Series Time Series Forecasting

Uncovering Insurance Fraud Conspiracy with Network Learning

no code implementations27 Feb 2020 Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Xiaolong Li, Shuang Yang, Yuan Qi

In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information.

Fraud Detection Graph Learning

Heterogeneous Graph Neural Networks for Malicious Account Detection

1 code implementation27 Feb 2020 Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, Le Song

We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform.

A Time Attention based Fraud Transaction Detection Framework

no code implementations26 Dec 2019 Longfei Li, Ziqi Liu, Chaochao Chen, Ya-Lin Zhang, Jun Zhou, Xiaolong Li

With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security.

Category-Level Articulated Object Pose Estimation

1 code implementation CVPR 2020 Xiaolong Li, He Wang, Li Yi, Leonidas Guibas, A. Lynn Abbott, Shuran Song

We develop a deep network based on PointNet++ that predicts ANCSH from a single depth point cloud, including part segmentation, normalized coordinates, and joint parameters in the canonical object space.

Pose Estimation

TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial

no code implementations18 Jun 2019 Shaosheng Cao, Xinxing Yang, Cen Chen, Jun Zhou, Xiaolong Li, Yuan Qi

With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business.

Fraud Detection

Review Helpfulness Prediction with Embedding-Gated CNN

no code implementations29 Aug 2018 Cen Chen, Minghui Qiu, Yinfei Yang, Jun Zhou, Jun Huang, Xiaolong Li, Forrest Bao

Product reviews, in the form of texts dominantly, significantly help consumers finalize their purchasing decisions.

A Boosting Framework of Factorization Machine

no code implementations17 Apr 2018 Longfei Li, Peilin Zhao, Jun Zhou, Xiaolong Li

However, to choose the rank properly, it usually needs to run the algorithm for many times using different ranks, which clearly is inefficient for some large-scale datasets.

Recommendation Systems

Feature Propagation on Graph: A New Perspective to Graph Representation Learning

no code implementations17 Apr 2018 Biao Xiang, Ziqi Liu, Jun Zhou, Xiaolong Li

In this paper, we first define the concept of feature propagation on graph formally, and then study its convergence conditions to equilibrium states.

Graph Embedding Graph Representation Learning

Distributed Collaborative Hashing and Its Applications in Ant Financial

no code implementations13 Apr 2018 Chaochao Chen, Ziqi Liu, Peilin Zhao, Longfei Li, Jun Zhou, Xiaolong Li

The experimental results demonstrate that, comparing with the classic and state-of-the-art (distributed) latent factor models, DCH has comparable performance in terms of recommendation accuracy but has both fast convergence speed in offline model training procedure and realtime efficiency in online recommendation procedure.

Collaborative Filtering

Time-sensitive Customer Churn Prediction based on PU Learning

no code implementations27 Feb 2018 Li Wang, Chaochao Chen, Jun Zhou, Xiaolong Li

With the fast development of Internet companies throughout the world, customer churn has become a serious concern.

Non-Local Graph-Based Prediction For Reversible Data Hiding In Images

no code implementations20 Feb 2018 Qi Chang, Gene Cheung, Yao Zhao, Xiaolong Li, Rongrong Ni

If sufficiently smooth, we pose a maximum a posteriori (MAP) problem using either a quadratic Laplacian regularizer or a graph total variation (GTV) term as signal prior.

GeniePath: Graph Neural Networks with Adaptive Receptive Paths

2 code implementations3 Feb 2018 Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi

We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data.

Secure Detection of Image Manipulation by means of Random Feature Selection

no code implementations2 Feb 2018 Zhipeng Chen, Benedetta Tondi, Xiaolong Li, Rongrong Ni, Yao Zhao, Mauro Barni

We address the problem of data-driven image manipulation detection in the presence of an attacker with limited knowledge about the detector.

Cryptography and Security

Small-footprint Keyword Spotting Using Deep Neural Network and Connectionist Temporal Classifier

no code implementations12 Sep 2017 Zhiming Wang, Xiaolong Li, Jun Zhou

Mainly for the sake of solving the lack of keyword-specific data, we propose one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and Connectionist Temporal Classifier (CTC) on power-constrained small-footprint mobile devices, taking full advantage of general corpus from continuous speech recognition which is of great amount.

Decision Making Small-Footprint Keyword Spotting +1

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