Search Results for author: Hongxia Yang

Found 86 papers, 41 papers with code

Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens

1 code implementation CVPR 2023 Yuxiao Chen, Jianbo Yuan, Yu Tian, Shijie Geng, Xinyu Li, Ding Zhou, Dimitris N. Metaxas, Hongxia Yang

However, direct aligning cross-modal information using such representations is challenging, as visual patches and text tokens differ in semantic levels and granularities.

Contrastive Learning

Intra-session Context-aware Feed Recommendation in Live Systems

no code implementations30 Sep 2022 Luo Ji, Gao Liu, Mingyang Yin, Hongxia Yang

Feed recommendation allows users to constantly browse items until feel uninterested and leave the session, which differs from traditional recommendation scenarios.

DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization

1 code implementation12 Sep 2022 Zheqi Lv, Wenqiao Zhang, Shengyu Zhang, Kun Kuang, Feng Wang, Yongwei Wang, Zhengyu Chen, Tao Shen, Hongxia Yang, Beng Chin Ooi, Fei Wu

DUET is deployed on a powerful cloud server that only requires the low cost of forwarding propagation and low time delay of data transmission between the device and the cloud.

Device-Cloud Collaboration Domain Adaptation +3

Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling

no code implementations19 Aug 2022 Zheqi Lv, Feng Wang, Shengyu Zhang, Kun Kuang, Hongxia Yang, Fei Wu

In this paper, we propose a novel approach that significantly improves the recommendation performance of the tail users while achieving at least comparable performance for the head users over the base model.

Recommendation Systems

Prompt Tuning for Generative Multimodal Pretrained Models

1 code implementation4 Aug 2022 Hao Yang, Junyang Lin, An Yang, Peng Wang, Chang Zhou, Hongxia Yang

Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural language pretraining and even vision pretraining.

Image Captioning Visual Entailment +1

Single Stage Virtual Try-on via Deformable Attention Flows

1 code implementation19 Jul 2022 Shuai Bai, Huiling Zhou, Zhikang Li, Chang Zhou, Hongxia Yang

Virtual try-on aims to generate a photo-realistic fitting result given an in-shop garment and a reference person image.

Image Animation Virtual Try-on

Device-Cloud Collaborative Recommendation via Meta Controller

no code implementations7 Jul 2022 Jiangchao Yao, Feng Wang, Xichen Ding, Shaohu Chen, Bo Han, Jingren Zhou, Hongxia Yang

To overcome this issue, we propose a meta controller to dynamically manage the collaboration between the on-device recommender and the cloud-based recommender, and introduce a novel efficient sample construction from the causal perspective to solve the dataset absence issue of meta controller.

Device-Cloud Collaboration

Knowledge Distillation of Transformer-based Language Models Revisited

no code implementations29 Jun 2022 Chengqiang Lu, Jianwei Zhang, Yunfei Chu, Zhengyu Chen, Jingren Zhou, Fei Wu, Haiqing Chen, Hongxia Yang

In the past few years, transformer-based pre-trained language models have achieved astounding success in both industry and academia.

Knowledge Distillation Language Modelling

Instance-wise Prompt Tuning for Pretrained Language Models

no code implementations4 Jun 2022 Yuezihan Jiang, Hao Yang, Junyang Lin, Hanyu Zhao, An Yang, Chang Zhou, Hongxia Yang, Zhi Yang, Bin Cui

Prompt Learning has recently gained great popularity in bridging the gap between pretraining tasks and various downstream tasks.

GraphMAE: Self-Supervised Masked Graph Autoencoders

3 code implementations22 May 2022 Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang

Despite this, contrastive learning-which heavily relies on structural data augmentation and complicated training strategies-has been the dominant approach in graph SSL, while the progress of generative SSL on graphs, especially graph autoencoders (GAEs), has thus far not reached the potential as promised in other fields.

Contrastive Learning Graph Classification +4

M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems

no code implementations17 May 2022 Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, Hongxia Yang

Industrial recommender systems have been growing increasingly complex, may involve \emph{diverse domains} such as e-commerce products and user-generated contents, and can comprise \emph{a myriad of tasks} such as retrieval, ranking, explanation generation, and even AI-assisted content production.

Explanation Generation Language Modelling +2

In-N-Out Generative Learning for Dense Unsupervised Video Segmentation

1 code implementation29 Mar 2022 Xiao Pan, Peike Li, Zongxin Yang, Huiling Zhou, Chang Zhou, Hongxia Yang, Jingren Zhou, Yi Yang

By contrast, pixel-level optimization is more explicit, however, it is sensitive to the visual quality of training data and is not robust to object deformation.

Contrastive Learning Semantic Segmentation +3

Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)

no code implementations23 Mar 2022 Yu Huang, Junyang Lin, Chang Zhou, Hongxia Yang, Longbo Huang

Recently, it has been observed that the best uni-modal network outperforms the jointly trained multi-modal network, which is counter-intuitive since multiple signals generally bring more information.

Cross-domain User Preference Learning for Cold-start Recommendation

no code implementations7 Dec 2021 Huiling Zhou, Jie Liu, Zhikang Li, Jin Yu, Hongxia Yang

With user history represented by a domain-aware sequential model, a frequency encoder is applied to the underlying tags for user content preference learning.

Recommendation Systems

KNAS: Green Neural Architecture Search

1 code implementation26 Nov 2021 Jingjing Xu, Liang Zhao, Junyang Lin, Rundong Gao, Xu sun, Hongxia Yang

Many existing neural architecture search (NAS) solutions rely on downstream training for architecture evaluation, which takes enormous computations.

Image Classification Neural Architecture Search +2

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

1 code implementation11 Nov 2021 Jiangchao Yao, Shengyu Zhang, Yang Yao, Feng Wang, Jianxin Ma, Jianwei Zhang, Yunfei Chu, Luo Ji, Kunyang Jia, Tao Shen, Anpeng Wu, Fengda Zhang, Ziqi Tan, Kun Kuang, Chao Wu, Fei Wu, Jingren Zhou, Hongxia Yang

However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.

Cloud Computing Edge-computing

Stable Prediction on Graphs with Agnostic Distribution Shift

no code implementations8 Oct 2021 Shengyu Zhang, Kun Kuang, Jiezhong Qiu, Jin Yu, Zhou Zhao, Hongxia Yang, Zhongfei Zhang, Fei Wu

The results demonstrate that our method outperforms various SOTA GNNs for stable prediction on graphs with agnostic distribution shift, including shift caused by node labels and attributes.

Graph Learning Recommendation Systems

M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining

no code implementations8 Oct 2021 Junyang Lin, An Yang, Jinze Bai, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang, Yong Li, Wei Lin, Jingren Zhou, Hongxia Yang

Recent expeditious developments in deep learning algorithms, distributed training, and even hardware design for large models have enabled training extreme-scale models, say GPT-3 and Switch Transformer possessing hundreds of billions or even trillions of parameters.

$\alpha$-Weighted Federated Adversarial Training

no code implementations29 Sep 2021 Jianing Zhu, Jiangchao Yao, Tongliang Liu, Kunyang Jia, Jingren Zhou, Bo Han, Hongxia Yang

Federated Adversarial Training (FAT) helps us address the data privacy and governance issues, meanwhile maintains the model robustness to the adversarial attack.

Adversarial Attack Federated Learning

Dynamic Sequential Graph Learning for Click-Through Rate Prediction

no code implementations26 Sep 2021 Yunfei Chu, xiaofu Chang, Kunyang Jia, Jingzhen Zhou, Hongxia Yang

In this paper, we propose a novel method, named Dynamic Sequential Graph Learning (DSGL), to enhance users or items' representations by utilizing collaborative information from the local sub-graphs associated with users or items.

Click-Through Rate Prediction Graph Learning +1

MC$^2$-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation

no code implementations25 Sep 2021 Zeyuan Chen, Jiangchao Yao, Feng Wang, Kunyang Jia, Bo Han, Wei zhang, Hongxia Yang

With the hardware development of mobile devices, it is possible to build the recommendation models on the mobile side to utilize the fine-grained features and the real-time feedbacks.

Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation

no code implementations20 Aug 2021 Luo Ji, Qin Qi, Bingqing Han, Hongxia Yang

In RL-LTV, the critic studies historical trajectories of items and predict the future LTV of fresh item, while the actor suggests a score-based policy which maximizes the future LTV expectation.

Recommendation Systems reinforcement-learning +1

Reliable Adversarial Distillation with Unreliable Teachers

2 code implementations ICLR 2022 Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang

However, when considering adversarial robustness, teachers may become unreliable and adversarial distillation may not work: teachers are pretrained on their own adversarial data, and it is too demanding to require that teachers are also good at every adversarial data queried by students.

Adversarial Robustness

Learning to Rehearse in Long Sequence Memorization

no code implementations2 Jun 2021 Zhu Zhang, Chang Zhou, Jianxin Ma, Zhijie Lin, Jingren Zhou, Hongxia Yang, Zhou Zhao

Further, we design a history sampler to select informative fragments for rehearsal training, making the memory focus on the crucial information.

Memorization Question Answering +1

Controllable Gradient Item Retrieval

1 code implementation31 May 2021 Haonan Wang, Chang Zhou, Carl Yang, Hongxia Yang, Jingrui He

A better way is to present a sequence of products with increasingly floral attributes based on the white dress, and allow the customer to select the most satisfactory one from the sequence.

Disentanglement Retrieval

Connecting Language and Vision for Natural Language-Based Vehicle Retrieval

1 code implementation31 May 2021 Shuai Bai, Zhedong Zheng, Xiaohan Wang, Junyang Lin, Zhu Zhang, Chang Zhou, Yi Yang, Hongxia Yang

In this paper, we apply one new modality, i. e., the language description, to search the vehicle of interest and explore the potential of this task in the real-world scenario.

Language Modelling Management +2

Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation

no code implementations Findings (ACL) 2021 Peng Wang, Junyang Lin, An Yang, Chang Zhou, Yichang Zhang, Jingren Zhou, Hongxia Yang

Experimental results demonstrate that our method outperforms the previous state-of-the-art methods in both automatic and human evaluation, especially on coverage and faithfulness.

Descriptive Table-to-Text Generation

M6-T: Exploring Sparse Expert Models and Beyond

no code implementations31 May 2021 An Yang, Junyang Lin, Rui Men, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang, Jiamang Wang, Yong Li, Di Zhang, Wei Lin, Lin Qu, Jingren Zhou, Hongxia Yang

Mixture-of-Experts (MoE) models can achieve promising results with outrageous large amount of parameters but constant computation cost, and thus it has become a trend in model scaling.

Playing the Game of 2048

M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers

no code implementations NeurIPS 2021 Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang

Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.

Image Generation

Learning Relation Alignment for Calibrated Cross-modal Retrieval

1 code implementation ACL 2021 Shuhuai Ren, Junyang Lin, Guangxiang Zhao, Rui Men, An Yang, Jingren Zhou, Xu sun, Hongxia Yang

To bridge the semantic gap between the two modalities, previous studies mainly focus on word-region alignment at the object level, lacking the matching between the linguistic relation among the words and the visual relation among the regions.

Cross-Modal Retrieval Image-to-Text Retrieval +3

Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation

1 code implementation28 May 2021 Yongji Wu, Defu Lian, Neil Zhenqiang Gong, Lu Yin, Mingyang Yin, Jingren Zhou, Hongxia Yang

Inspired by the idea of vector quantization that uses cluster centroids to approximate items, we propose LISA (LInear-time Self Attention), which enjoys both the effectiveness of vanilla self-attention and the efficiency of sparse attention.

Quantization Sequential Recommendation

Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention

no code implementations28 May 2021 Yongji Wu, Lu Yin, Defu Lian, Mingyang Yin, Neil Zhenqiang Gong, Jingren Zhou, Hongxia Yang

With the rapid development of these services in the last two decades, users have accumulated a massive amount of behavior data.

Sequential Recommendation

CogView: Mastering Text-to-Image Generation via Transformers

4 code implementations NeurIPS 2021 Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang

Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding.

Ranked #54 on Text-to-Image Generation on COCO (using extra training data)

Super-Resolution Zero-Shot Text-to-Image Generation

Federated Graph Learning -- A Position Paper

no code implementations24 May 2021 Huanding Zhang, Tao Shen, Fei Wu, Mingyang Yin, Hongxia Yang, Chao Wu

Federated learning (FL) is a an emerging technique that can collaboratively train a shared model while keeping the data decentralized, which is a rational solution for distributed GNN training.

Federated Learning Graph Learning

UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis

no code implementations NeurIPS 2021 Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang

Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.

Image Generation

TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

1 code implementation17 May 2021 Lu Wang, xiaofu Chang, Shuang Li, Yunfei Chu, Hui Li, Wei zhang, Xiaofeng He, Le Song, Jingren Zhou, Hongxia Yang

Secondly, on top of the proposed graph transformer, we introduce a two-stream encoder that separately extracts representations from temporal neighborhoods associated with the two interaction nodes and then utilizes a co-attentional transformer to model inter-dependencies at a semantic level.

Contrastive Learning Graph Learning +2

Contrastive Attraction and Contrastive Repulsion for Representation Learning

1 code implementation8 May 2021 Huangjie Zheng, Xu Chen, Jiangchao Yao, Hongxia Yang, Chunyuan Li, Ya zhang, Hao Zhang, Ivor Tsang, Jingren Zhou, Mingyuan Zhou

We realize this strategy with contrastive attraction and contrastive repulsion (CACR), which makes the query not only exert a greater force to attract more distant positive samples but also do so to repel closer negative samples.

Contrastive Learning Representation Learning

Device-Cloud Collaborative Learning for Recommendation

no code implementations14 Apr 2021 Jiangchao Yao, Feng Wang, Kunyang Jia, Bo Han, Jingren Zhou, Hongxia Yang

With the rapid development of storage and computing power on mobile devices, it becomes critical and popular to deploy models on devices to save onerous communication latencies and to capture real-time features.

Learning with Group Noise

no code implementations17 Mar 2021 Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han

Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels.

Learning with noisy labels

OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge Services

1 code implementation3 Mar 2021 Xiao Liu, Da Yin, Jingnan Zheng, Xingjian Zhang, Peng Zhang, Hongxia Yang, Yuxiao Dong, Jie Tang

Academic knowledge services have substantially facilitated the development of the science enterprise by providing a plenitude of efficient research tools.

Language Modelling Link Prediction

CogDL: A Comprehensive Library for Graph Deep Learning

1 code implementation1 Mar 2021 Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Zhongming Yu, Hengrui Zhang, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Yuxiao Dong, Yang Yang, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, Jie Tang

In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.

Graph Classification Graph Embedding +5

M6: A Chinese Multimodal Pretrainer

no code implementations1 Mar 2021 Junyang Lin, Rui Men, An Yang, Chang Zhou, Ming Ding, Yichang Zhang, Peng Wang, Ang Wang, Le Jiang, Xianyan Jia, Jie Zhang, Jianwei Zhang, Xu Zou, Zhikang Li, Xiaodong Deng, Jie Liu, Jinbao Xue, Huiling Zhou, Jianxin Ma, Jin Yu, Yong Li, Wei Lin, Jingren Zhou, Jie Tang, Hongxia Yang

In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1. 9TB images and 292GB texts that cover a wide range of domains.

Image Generation

Dynamic Memory based Attention Network for Sequential Recommendation

1 code implementation18 Feb 2021 Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, Xia Hu

It segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users.

Sequential Recommendation

Sparse-Interest Network for Sequential Recommendation

1 code implementation18 Feb 2021 Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, Xia Hu

Our sparse-interest module can adaptively infer a sparse set of concepts for each user from the large concept pool and output multiple embeddings accordingly.

Sequential Recommendation

Deep Co-Attention Network for Multi-View Subspace Learning

1 code implementation15 Feb 2021 Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, Jingrui He

For example, given the diagnostic result that a model provided based on the X-ray images of a patient at different poses, the doctor needs to know why the model made such a prediction.

Inductive Granger Causal Modeling for Multivariate Time Series

no code implementations10 Feb 2021 Yunfei Chu, Xiaowei Wang, Jianxin Ma, Kunyang Jia, Jingren Zhou, Hongxia Yang

To bridge this gap, we propose an Inductive GRanger cAusal modeling (InGRA) framework for inductive Granger causality learning and common causal structure detection on multivariate time series, which exploits the shared commonalities underlying the different individuals.

Time Series Time Series Analysis

Understanding the Interaction of Adversarial Training with Noisy Labels

no code implementations6 Feb 2021 Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan Kankanhalli, Masashi Sugiyama

A recent adversarial training (AT) study showed that the number of projected gradient descent (PGD) steps to successfully attack a point (i. e., find an adversarial example in its proximity) is an effective measure of the robustness of this point.

Local Clustering Graph Neural Networks

no code implementations1 Jan 2021 Jiezhong Qiu, Yukuo Cen, Qibin Chen, Chang Zhou, Jingren Zhou, Hongxia Yang, Jie Tang

Based on the theoretical analysis, we propose Local Clustering Graph Neural Networks (LCGNN), a GNN learning paradigm that utilizes local clustering to efficiently search for small but compact subgraphs for GNN training and inference.


A Gradient-based Kernel Approach for Efficient Network Architecture Search

no code implementations1 Jan 2021 Jingjing Xu, Liang Zhao, Junyang Lin, Xu sun, Hongxia Yang

Inspired by our new finding, we explore a simple yet effective network architecture search (NAS) approach that leverages gradient correlation and gradient values to find well-performing architectures.

Image Classification text-classification +1

Continual Memory: Can We Reason After Long-Term Memorization?

no code implementations1 Jan 2021 Zhu Zhang, Chang Zhou, Zhou Zhao, Zhijie Lin, Jingren Zhou, Hongxia Yang

Existing reasoning tasks often follow the setting of "reasoning while experiencing", which has an important assumption that the raw contents can be always accessed while reasoning.


Counterfactual Prediction for Bundle Treatment

no code implementations NeurIPS 2020 Hao Zou, Peng Cui, Bo Li, Zheyan Shen, Jianxin Ma, Hongxia Yang, Yue He

Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields.

Decision Making Marketing +1

CogLTX: Applying BERT to Long Texts

1 code implementation NeurIPS 2020 Ming Ding, Chang Zhou, Hongxia Yang, Jie Tang

BERTs are incapable of processing long texts due to its quadratically increasing memory and time consumption.

text-classification Text Classification

Graph-based Multi-hop Reasoning for Long Text Generation

no code implementations28 Sep 2020 Liang Zhao, Jingjing Xu, Junyang Lin, Yichang Zhang, Hongxia Yang, Xu sun

The reasoning module is responsible for searching skeleton paths from a knowledge graph to imitate the imagination process in the human writing for semantic transfer.

Review Generation Story Generation

DVE: Dynamic Variational Embeddings with Applications in Recommender Systems

no code implementations27 Aug 2020 Meimei Liu, Hongxia Yang

Embedding is a useful technique to project a high-dimensional feature into a low-dimensional space, and it has many successful applications including link prediction, node classification and natural language processing.

Link Prediction Node Classification +1

Disentangled Self-Supervision in Sequential Recommenders

1 code implementation23 Aug 2020 Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, Wenwu Zhu

There exist two challenges: i) reconstructing a future sequence containing many behaviors is exponentially harder than reconstructing a single next behavior, which can lead to difficulty in convergence, and ii) the sequence of all future behaviors can involve many intentions, not all of which may be predictable from the sequence of earlier behaviors.


DeVLBert: Learning Deconfounded Visio-Linguistic Representations

1 code implementation16 Aug 2020 Shengyu Zhang, Tan Jiang, Tan Wang, Kun Kuang, Zhou Zhao, Jianke Zhu, Jin Yu, Hongxia Yang, Fei Wu

In this paper, we propose to investigate the problem of out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned.

Image Retrieval Question Answering +2

Poet: Product-oriented Video Captioner for E-commerce

1 code implementation16 Aug 2020 Shengyu Zhang, Ziqi Tan, Jin Yu, Zhou Zhao, Kun Kuang, Jie Liu, Jingren Zhou, Hongxia Yang, Fei Wu

Then, based on the aspects of the video-associated product, we perform knowledge-enhanced spatial-temporal inference on those graphs for capturing the dynamic change of fine-grained product-part characteristics.

Video Captioning

A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning

no code implementations19 Jul 2020 Xuandong Zhao, Jinbao Xue, Jin Yu, Xi Li, Hongxia Yang

In real-world applications, networks usually consist of billions of various types of nodes and edges with abundant attributes.

Link Prediction Network Embedding

Comprehensive Information Integration Modeling Framework for Video Titling

1 code implementation24 Jun 2020 Shengyu Zhang, Ziqi Tan, Jin Yu, Zhou Zhao, Kun Kuang, Tan Jiang, Jingren Zhou, Hongxia Yang, Fei Wu

In e-commerce, consumer-generated videos, which in general deliver consumers' individual preferences for the different aspects of certain products, are massive in volume.

Descriptive Video Captioning

Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems

no code implementations20 May 2020 Chang Zhou, Jianxin Ma, Jianwei Zhang, Jingren Zhou, Hongxia Yang

Deep candidate generation (DCG) that narrows down the collection of relevant items from billions to hundreds via representation learning has become prevalent in industrial recommender systems.

Contrastive Learning Fairness +3

Understanding Negative Sampling in Graph Representation Learning

4 code implementations20 May 2020 Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, Jie Tang

To the best of our knowledge, we are the first to derive the theory and quantify that the negative sampling distribution should be positively but sub-linearly correlated to their positive sampling distribution.

Graph Learning Graph Representation Learning +2

Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities

no code implementations ICLR 2020 Baichuan Yuan, Xiaowei Wang, Jianxin Ma, Chang Zhou, Andrea L. Bertozzi, Hongxia Yang

To bridge this gap, we introduce a declustering based hidden variable model that leads to an efficient inference procedure via a variational autoencoder (VAE).

Collaborative Filtering Point Processes +1

InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining

no code implementations30 Mar 2020 Junyang Lin, An Yang, Yichang Zhang, Jie Liu, Jingren Zhou, Hongxia Yang

We pretrain the model with three pretraining tasks, including masked segment modeling (MSM), masked region modeling (MRM) and image-text matching (ITM); and finetune the model on a series of vision-and-language downstream tasks.

Image Retrieval Image-text matching +3

Learning to Hash with Graph Neural Networks for Recommender Systems

no code implementations4 Mar 2020 Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, Xia Hu

In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes.

Graph Representation Learning Recommendation Systems +1

Grounded and Controllable Image Completion by Incorporating Lexical Semantics

no code implementations29 Feb 2020 Shengyu Zhang, Tan Jiang, Qinghao Huang, Ziqi Tan, Zhou Zhao, Siliang Tang, Jin Yu, Hongxia Yang, Yi Yang, Fei Wu

Existing image completion procedure is highly subjective by considering only visual context, which may trigger unpredictable results which are plausible but not faithful to a grounded knowledge.

Learning Disentangled Representations for Recommendation

no code implementations NeurIPS 2019 Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu

Our approach achieves macro disentanglement by inferring the high-level concepts associated with user intentions (e. g., to buy a shirt or a cellphone), while capturing the preference of a user regarding the different concepts separately.

Decision Making Disentanglement +1

Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series

no code implementations25 Sep 2019 Yunfei Chu, Xiaowei Wang, Chunyan Feng, Jianxin Ma, Jingren Zhou, Hongxia Yang

Granger causal structure reconstruction is an emerging topic that can uncover causal relationship behind multivariate time series data.

Time Series Time Series Analysis

Deep Expectation-Maximization in Hidden Markov Models via Simultaneous Perturbation Stochastic Approximation

no code implementations25 Sep 2019 Chong Li, Dan Shen, C.J. Richard Shi, Hongxia Yang

We propose a novel method to estimate the parameters of a collection of Hidden Markov Models (HMM), each of which corresponds to a set of known features.

Dimensional Reweighting Graph Convolution Networks

no code implementations25 Sep 2019 Xu Zou, Qiuye Jia, Jianwei Zhang, Chang Zhou, Zijun Yao, Hongxia Yang, Jie Tang

In this paper, we propose a method named Dimensional reweighting Graph Convolutional Networks (DrGCNs), to tackle the problem of variance between dimensional information in the node representations of GCNs.

Node Classification

Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks

1 code implementation28 Aug 2019 Yuting Ye, Xuwu Wang, Jiangchao Yao, Kunyang Jia, Jingren Zhou, Yanghua Xiao, Hongxia Yang

Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to content recommendation.

General Classification Knowledge Graph Embedding +3

Towards Knowledge-Based Recommender Dialog System

1 code implementation IJCNLP 2019 Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, Jie Tang

In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System.

Recommendation Systems Text Generation

Dimensional Reweighting Graph Convolutional Networks

2 code implementations4 Jul 2019 Xu Zou, Qiuye Jia, Jianwei Zhang, Chang Zhou, Hongxia Yang, Jie Tang

Graph Convolution Networks (GCNs) are becoming more and more popular for learning node representations on graphs.

Node Classification

Cognitive Knowledge Graph Reasoning for One-shot Relational Learning

1 code implementation13 Jun 2019 Zhengxiao Du, Chang Zhou, Ming Ding, Hongxia Yang, Jie Tang

Inferring new facts from existing knowledge graphs (KG) with explainable reasoning processes is a significant problem and has received much attention recently.

Knowledge Graphs Relational Reasoning +1

Sequential Scenario-Specific Meta Learner for Online Recommendation

1 code implementation2 Jun 2019 Zhengxiao Du, Xiaowei Wang, Hongxia Yang, Jingren Zhou, Jie Tang

Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks.

Few-Shot Learning

Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding

1 code implementation25 May 2019 Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, Xia Hu

Network embedding models are powerful tools in mapping nodes in a network into continuous vector-space representations in order to facilitate subsequent tasks such as classification and link prediction.

General Classification Language Modelling +3

Towards Knowledge-Based Personalized Product Description Generation in E-commerce

4 code implementations29 Mar 2019 Qibin Chen, Junyang Lin, Yichang Zhang, Hongxia Yang, Jingren Zhou, Jie Tang

In order to make the description both informative and personalized, KOBE considers a variety of important factors during text generation, including product aspects, user categories, and knowledge base, etc.

Text Generation

AliGraph: A Comprehensive Graph Neural Network Platform

no code implementations23 Feb 2019 Rong Zhu, Kun Zhao, Hongxia Yang, Wei. Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou

An increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relationship among potentially billions of elements.

Distributed, Parallel, and Cluster Computing

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