Search Results for author: Enhong Chen

Found 120 papers, 48 papers with code

Large Language Model based Long-tail Query Rewriting in Taobao Search

no code implementations7 Nov 2023 Wenjun Peng, Guiyang Li, Yue Jiang, Zilong Wang, Dan Ou, Xiaoyi Zeng, Derong Xu, Tongxu, Enhong Chen

In the realm of e-commerce search, the significance of semantic matching cannot be overstated, as it directly impacts both user experience and company revenue.

Contrastive Learning Language Modelling +2

APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation

1 code implementation6 Nov 2023 Mingjia Yin, Hao Wang, Xiang Xu, Likang Wu, Sirui Zhao, Wei Guo, Yong liu, Ruiming Tang, Defu Lian, Enhong Chen

To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems.

Graph Learning Multi-Task Learning +1

Generative Input: Towards Next-Generation Input Methods Paradigm

no code implementations2 Nov 2023 Keyu Ding, Yongcan Wang, Zihang Xu, Zhenzhen Jia, Shijin Wang, Cong Liu, Enhong Chen

The results demonstrate that we have achieved state-of-the-art performance for the first time in the Full-mode Key-sequence to Characters(FK2C) task.

Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems

no code implementations1 Nov 2023 Hao Zhang, Mingyue Cheng, Qi Liu, Zhiding Liu, Enhong Chen

Sequential recommender systems (SRS) have gained widespread popularity in recommendation due to their ability to effectively capture dynamic user preferences.

Future prediction Sequential Recommendation

Woodpecker: Hallucination Correction for Multimodal Large Language Models

1 code implementation24 Oct 2023 Shukang Yin, Chaoyou Fu, Sirui Zhao, Tong Xu, Hao Wang, Dianbo Sui, Yunhang Shen, Ke Li, Xing Sun, Enhong Chen

Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with the image content.

Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment

1 code implementation21 Oct 2023 Chuang Zhao, Hongke Zhao, HengShu Zhu, Zhenya Huang, Nan Feng, Enhong Chen, Hui Xiong

One prevalent solution is the bi-discriminator domain adversarial network, which strives to identify target domain samples outside the support of the source domain distribution and enforces their classification to be consistent on both discriminators.

Contrastive Learning Learning Theory +1

Large-Scale OD Matrix Estimation with A Deep Learning Method

no code implementations9 Oct 2023 Zheli Xiong, Defu Lian, Enhong Chen, Gang Chen, Xiaomin Cheng

To alleviate this problem, some researchers incorporate a prior OD matrix as a target in the regression to provide more structural constraints.

Interactive Graph Convolutional Filtering

no code implementations4 Sep 2023 Jin Zhang, Defu Lian, Hong Xie, Yawen Li, Enhong Chen

Furthermore, we employ Bayesian meta-learning methods to effectively address the cold-start problem and derive theoretical regret bounds for our proposed method, ensuring a robust performance guarantee.

Collaborative Filtering Meta-Learning +2

Identifiable Cognitive Diagnosis with Encoder-decoder for Modelling Students' Performance

no code implementations1 Sep 2023 Jiatong Li, Qi Liu, Fei Wang, Jiayu Liu, Zhenya Huang, Enhong Chen

Cognitive diagnosis aims to diagnose students' knowledge proficiencies based on their response scores on exam questions, which is the basis of many domains such as computerized adaptive testing.

KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification

no code implementations15 Aug 2023 Likang Wu, Junji Jiang, Hongke Zhao, Hao Wang, Defu Lian, Mengdi Zhang, Enhong Chen

However, the multi-faceted semantic orientation in the feature-semantic alignment has been neglected by previous work, i. e. the content of a node usually covers diverse topics that are relevant to the semantics of multiple labels.

Node Classification Representation Learning +1

Multi-Grained Multimodal Interaction Network for Entity Linking

1 code implementation19 Jul 2023 Pengfei Luo, Tong Xu, Shiwei Wu, Chen Zhu, Linli Xu, Enhong Chen

Then, to derive the similarity matching score for each mention-entity pair, we device three interaction units to comprehensively explore the intra-modal interaction and inter-modal fusion among features of entities and mentions.

Contrastive Learning Descriptive +1

Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms

1 code implementation14 Jul 2023 Qi Liu, Zheng Gong, Zhenya Huang, Chuanren Liu, HengShu Zhu, Zhi Li, Enhong Chen, Hui Xiong

Machine learning algorithms have become ubiquitous in a number of applications (e. g. image classification).

Image Classification

A DeepLearning Framework for Dynamic Estimation of Origin-Destination Sequence

no code implementations11 Jul 2023 Zheli Xiong, Defu Lian, Enhong Chen, Gang Chen, Xiaomin Cheng

To this end, this paper proposes an integrated method, which uses deep learning methods to infer the structure of OD sequence and uses structural constraints to guide traditional numerical optimization.

Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations

no code implementations10 Jul 2023 Likang Wu, Zhaopeng Qiu, Zhi Zheng, HengShu Zhu, Enhong Chen

This paper focuses on unveiling the capability of large language models in understanding behavior graphs and leveraging this understanding to enhance recommendations in online recruitment, including the promotion of out-of-distribution (OOD) application.

Language Modelling Large Language Model +1

A Solution to CVPR'2023 AQTC Challenge: Video Alignment for Multi-Step Inference

1 code implementation26 Jun 2023 Chao Zhang, Shiwei Wu, Sirui Zhao, Tong Xu, Enhong Chen

In this paper, we present a solution for enhancing video alignment to improve multi-step inference.

Video Alignment

A Survey on Multimodal Large Language Models

1 code implementation23 Jun 2023 Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing Sun, Tong Xu, Enhong Chen

Multimodal Large Language Model (MLLM) recently has been a new rising research hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform multimodal tasks.

Language Modelling Large Language Model +2

A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design

no code implementations20 Jun 2023 Zaixi Zhang, Jiaxian Yan, Qi Liu, Enhong Chen, Marinka Zitnik

Recent developments in geometric deep learning, focusing on the integration and processing of 3D geometric data, coupled with the availability of accurate protein 3D structure predictions from tools like AlphaFold, have greatly advanced the field of structure-based drug design.

Benchmarking Drug Discovery +1

Efficiently Measuring the Cognitive Ability of LLMs: An Adaptive Testing Perspective

1 code implementation18 Jun 2023 Yan Zhuang, Qi Liu, Yuting Ning, Weizhe Huang, Rui Lv, Zhenya Huang, Guanhao Zhao, Zheng Zhang, Qingyang Mao, Shijin Wang, Enhong Chen

Different tests for different models using efficient adaptive testing -- we believe this has the potential to become a new norm in evaluating large language models.

Mathematical Reasoning Test

Description-Enhanced Label Embedding Contrastive Learning for Text Classification

1 code implementation15 Jun 2023 Kun Zhang, Le Wu, Guangyi Lv, Enhong Chen, Shulan Ruan, Jing Liu, Zhiqiang Zhang, Jun Zhou, Meng Wang

Then, we propose a novel Relation of Relation Learning Network (R2-Net) for text classification, in which text classification and R2 classification are treated as optimization targets.

Contrastive Learning Self-Supervised Learning +2

Multi-Temporal Relationship Inference in Urban Areas

1 code implementation15 Jun 2023 Shuangli Li, Jingbo Zhou, Ji Liu, Tong Xu, Enhong Chen, Hui Xiong

Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL).

Graph Learning Representation Learning +1

Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation

no code implementations14 Jun 2023 Likang Wu, Zhi Li, Hongke Zhao, Zhefeng Wang, Qi Liu, Baoxing Huai, Nicholas Jing Yuan, Enhong Chen

Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously.

Knowledge Graphs Zero-Shot Learning

A Survey on Large Language Models for Recommendation

1 code implementation31 May 2023 Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, HengShu Zhu, Qi Liu, Hui Xiong, Enhong Chen

Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS).

Recommendation Systems Self-Supervised Learning

An Equivariant Generative Framework for Molecular Graph-Structure Co-Design

no code implementations12 Apr 2023 Zaixi Zhang, Qi Liu, Chee-Kong Lee, Chang-Yu Hsieh, Enhong Chen

Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.

Drug Discovery Graph Generation +1

Quiz-based Knowledge Tracing

no code implementations5 Apr 2023 Shuanghong Shen, Enhong Chen, Bihan Xu, Qi Liu, Zhenya Huang, Linbo Zhu, Yu Su

In this paper, we present the Quiz-based Knowledge Tracing (QKT) model to monitor students' knowledge states according to their quiz-based learning interactions.

Decision Making Knowledge Tracing

AU-aware graph convolutional network for Macro- and Micro-expression spotting

1 code implementation16 Mar 2023 Shukang Yin, Shiwei Wu, Tong Xu, Shifeng Liu, Sirui Zhao, Enhong Chen

Automatic Micro-Expression (ME) spotting in long videos is a crucial step in ME analysis but also a challenging task due to the short duration and low intensity of MEs.

Micro-Expression Spotting

TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders

1 code implementation1 Mar 2023 Mingyue Cheng, Qi Liu, Zhiding Liu, Hao Zhang, Rujiao Zhang, Enhong Chen

In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks.

Time Series Time Series Analysis +1

GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation

no code implementations1 Mar 2023 Yongqiang Han, Likang Wu, Hao Wang, Guifeng Wang, Mengdi Zhang, Zhi Li, Defu Lian, Enhong Chen

Sequential Recommendation is a widely studied paradigm for learning users' dynamic interests from historical interactions for predicting the next potential item.

Contrastive Learning Sequential Recommendation

Federated Nearest Neighbor Machine Translation

no code implementations23 Feb 2023 Yichao Du, Zhirui Zhang, Bingzhe Wu, Lemao Liu, Tong Xu, Enhong Chen

To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention.

Federated Learning Machine Translation +4

FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification

no code implementations20 Feb 2023 Mingyue Cheng, Qi Liu, Zhiding Liu, Zhi Li, Yucong Luo, Enhong Chen

Deep learning-based algorithms, e. g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task.

Time Series Time Series Analysis +1

A Novel Approach for Auto-Formulation of Optimization Problems

no code implementations9 Feb 2023 Yuting Ning, Jiayu Liu, Longhu Qin, Tong Xiao, Shangzi Xue, Zhenya Huang, Qi Liu, Enhong Chen, Jinze Wu

In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the components of the optimization problem; subtask 2: generating formulations for the optimization problem.

Ensemble Learning named-entity-recognition +2

Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training

1 code implementation18 Jan 2023 Yuting Ning, Zhenya Huang, Xin Lin, Enhong Chen, Shiwei Tong, Zheng Gong, Shijin Wang

To this end, in this paper, we propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo, which attempts to bring questions with more similar purposes closer.

Contrastive Learning

More is Better: A Database for Spontaneous Micro-Expression with High Frame Rates

no code implementations3 Jan 2023 Sirui Zhao, Huaying Tang, Xinglong Mao, Shifeng Liu, Hanqing Tao, Hao Wang, Tong Xu, Enhong Chen

To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7, 526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years.

Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation

no code implementations15 Nov 2022 Zhihao Zhu, Chenwang Wu, Min Zhou, Hao Liao, Defu Lian, Enhong Chen

Recent studies show that Graph Neural Networks(GNNs) are vulnerable and easily fooled by small perturbations, which has raised considerable concerns for adapting GNNs in various safety-critical applications.

Adversarial Attack

Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF

no code implementations9 Nov 2022 Junzhe Jiang, Mingyue Cheng, Qi Liu, Zhi Li, Enhong Chen

Recognizing useful named entities plays a vital role in medical information processing, which helps drive the development of medical area research.

Medical Named Entity Recognition named-entity-recognition +3

One Person, One Model--Learning Compound Router for Sequential Recommendation

1 code implementation5 Nov 2022 Zhiding Liu, Mingyue Cheng, Zhi Li, Qi Liu, Enhong Chen

The core idea of CANet is to route the input user behaviors with a light-weighted router module.

Sequential Recommendation

Towards Robust Recommender Systems via Triple Cooperative Defense

no code implementations25 Oct 2022 Qingyang Wang, Defu Lian, Chenwang Wu, Enhong Chen

Notably, TCD adds pseudo label data instead of deleting abnormal data, which avoids the cleaning of normal data, and the cooperative training of the three models is also beneficial to model generalization.

Pseudo Label Recommendation Systems

Model Inversion Attacks against Graph Neural Networks

no code implementations16 Sep 2022 Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chee-Kong Lee, Enhong Chen

One famous privacy attack against data analysis models is the model inversion attack, which aims to infer sensitive data in the training dataset and leads to great privacy concerns.

Reinforcement Learning (RL)

Multi-modal Siamese Network for Entity Alignment

1 code implementation KDD 2022 Liyi Chen, Zhi Li, Tong Xu, Han Wu, Zhefeng Wang, Nicholas Jing Yuan, Enhong Chen

To deal with that problem, in this paper, we propose a novel Multi-modal Siamese Network for Entity Alignment (MSNEA) to align entities in different MMKGs, in which multi-modal knowledge could be comprehensively leveraged by the exploitation of inter-modal effect.

Ranked #7 on Multi-modal Entity Alignment on UMVM-oea-d-w-v1 (using extra training data)

Contrastive Learning Knowledge Graphs +2

Winning the CVPR'2022 AQTC Challenge: A Two-stage Function-centric Approach

1 code implementation20 Jun 2022 Shiwei Wu, Weidong He, Tong Xu, Hao Wang, Enhong Chen

Affordance-centric Question-driven Task Completion for Egocentric Assistant(AQTC) is a novel task which helps AI assistant learn from instructional videos and scripts and guide the user step-by-step.

Boosting Factorization Machines via Saliency-Guided Mixup

1 code implementation17 Jun 2022 Chenwang Wu, Defu Lian, Yong Ge, Min Zhou, Enhong Chen, DaCheng Tao

Second, considering that MixFM may generate redundant or even detrimental instances, we further put forward a novel Factorization Machine powered by Saliency-guided Mixup (denoted as SMFM).

Recommendation Systems

Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever

no code implementations30 May 2022 Jin Chen, Defu Lian, Yucheng Li, Baoyun Wang, Kai Zheng, Enhong Chen

Recommender retrievers aim to rapidly retrieve a fraction of items from the entire item corpus when a user query requests, with the representative two-tower model trained with the log softmax loss.

Non-Parametric Domain Adaptation for End-to-End Speech Translation

1 code implementation23 May 2022 Yichao Du, Weizhi Wang, Zhirui Zhang, Boxing Chen, Tong Xu, Jun Xie, Enhong Chen

End-to-End Speech Translation (E2E-ST) has received increasing attention due to the potential of its less error propagation, lower latency, and fewer parameters.

Domain Adaptation Translation

Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification

no code implementations18 May 2022 Kai Zhang, Qi Liu, Zhenya Huang, Mingyue Cheng, Kun Zhang, Mengdi Zhang, Wei Wu, Enhong Chen

Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i. e., the part-of-speech tags and dependency relations).

Classification Graph Attention +4

Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction

no code implementations18 Apr 2022 Likang Wu, Hao Wang, Enhong Chen, Zhi Li, Hongke Zhao, Jianhui Ma

To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i. e., preference topics generation, preference shift modeling, and social influence activation.

Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation

1 code implementation CVPR 2022 Lin Chen, Huaian Chen, Zhixiang Wei, Xin Jin, Xiao Tan, Yi Jin, Enhong Chen

Such NWD can be coupled with the classifier to serve as a discriminator satisfying the K-Lipschitz constraint without the requirements of additional weight clipping or gradient penalty strategy.

Unsupervised Domain Adaptation

Reinforcement Routing on Proximity Graph for Efficient Recommendation

no code implementations23 Jan 2022 Chao Feng, Defu Lian, Xiting Wang, Zheng Liu, Xing Xie, Enhong Chen

Instead of searching the nearest neighbor for the query, we search the item with maximum inner product with query on the proximity graph.

Imitation Learning Recommendation Systems

Online Allocation Problem with Two-sided Resource Constraints

no code implementations28 Dec 2021 Qixin Zhang, Wenbing Ye, Zaiyi Chen, Haoyuan Hu, Enhong Chen, Yang Yu

As a result, only limited violations of constraints or pessimistic competitive bounds could be guaranteed.

Decision Making Fairness +1

VIRT: Improving Representation-based Models for Text Matching through Virtual Interaction

no code implementations8 Dec 2021 Dan Li, Yang Yang, Hongyin Tang, Jingang Wang, Tong Xu, Wei Wu, Enhong Chen

With the booming of pre-trained transformers, representation-based models based on Siamese transformer encoders have become mainstream techniques for efficient text matching.

Text Matching

Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness

1 code implementation24 Oct 2021 Dazhong Shen, Chuan Qin, Chao Wang, HengShu Zhu, Enhong Chen, Hui Xiong

As one of the most popular generative models, Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference.

Variational Inference

Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering

1 code implementation13 Sep 2021 Jin Chen, Defu Lian, Binbin Jin, Xu Huang, Kai Zheng, Enhong Chen

Variational AutoEncoder (VAE) has been extended as a representative nonlinear method for collaborative filtering.

Collaborative Filtering

DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis

1 code implementation ICCV 2021 Shulan Ruan, Yong Zhang, Kun Zhang, Yanbo Fan, Fan Tang, Qi Liu, Enhong Chen

Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency.

Image Generation Sentence Embedding +1

SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation

no code implementations18 Aug 2021 Kai Zhang, Hao Qian, Qi Liu, Zhiqiang Zhang, Jun Zhou, Jianhui Ma, Enhong Chen

Specifically, we first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review.

Recommendation Systems

LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching

no code implementations6 Aug 2021 Kun Zhang, Guangyi Lv, Le Wu, Enhong Chen, Qi Liu, Meng Wang

In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations.

Language Modelling Natural Language Inference +1

DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching

no code implementations9 Jun 2021 Kun Zhang, Guangyi Lv, Meng Wang, Enhong Chen

Then, we develop a Dynamic Gaussian Attention (DGA) to dynamically capture the important parts and corresponding local contexts from a detailed perspective.

Language Modelling Representation Learning

GraphMI: Extracting Private Graph Data from Graph Neural Networks

1 code implementation5 Jun 2021 Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chengqiang Lu, Chuanren Liu, Enhong Chen

Then we design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.

Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective

no code implementations27 May 2021 Likang Wu, Zhi Li, Hongke Zhao, Qi Liu, Enhong Chen

Usually, this prediction is always with great challenges to making a comprehensive understanding of both the start-up project and market environment.

A Survey of Knowledge Tracing

1 code implementation6 May 2021 Qi Liu, Shuanghong Shen, Zhenya Huang, Enhong Chen, Yonghe Zheng

In contrast to traditional face-to-face classroom education, online education enables us to record and research a large amount of learning data for offering intelligent educational services.

Knowledge Tracing

XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction

1 code implementation22 Apr 2021 Runlong Yu, Yuyang Ye, Qi Liu, Zihan Wang, Chunfeng Yang, Yucheng Hu, Enhong Chen

Motivated by this, we propose a novel Extreme Cross Network, abbreviated XCrossNet, which aims at learning dense and sparse feature interactions in an explicit manner.

Click-Through Rate Prediction Feature Engineering +1

Towards Variable-Length Textual Adversarial Attacks

no code implementations16 Apr 2021 Junliang Guo, Zhirui Zhang, Linlin Zhang, Linli Xu, Boxing Chen, Enhong Chen, Weihua Luo

In this way, our approach is able to more comprehensively find adversarial examples around the decision boundary and effectively conduct adversarial attacks.

Machine Translation Translation

Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis

no code implementations27 Jan 2021 Yichao Du, Pengfei Luo, Xudong Hong, Tong Xu, Zhe Zhang, Chao Ren, Yi Zheng, Enhong Chen

Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making.

Decision Making

Learning the Implicit Semantic Representation on Graph-Structured Data

1 code implementation16 Jan 2021 Likang Wu, Zhi Li, Hongke Zhao, Qi Liu, Jun Wang, Mengdi Zhang, Enhong Chen

Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole, while the implicit semantic associations behind highly complex interactions of graphs are largely unexploited.

Representation Learning

Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing

no code implementations15 Jan 2021 Haoyang Bi, Haiping Ma, Zhenya Huang, Yu Yin, Qi Liu, Enhong Chen, Yu Su, Shijin Wang

In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models.

Active Learning

Adam revisited: a weighted past gradients perspective

no code implementations1 Jan 2021 Hui Zhong, Zaiyi Chen, Chuan Qin, Zai Huang, Vincent W. Zheng, Tong Xu, Enhong Chen

Though many algorithms, such as AMSGRAD and ADAMNC, have been proposed to fix the non-convergence issues, achieving a data-dependent regret bound similar to or better than ADAGRAD is still a challenge to these methods.

R$^2$-Net: Relation of Relation Learning Network for Sentence Semantic Matching

no code implementations16 Dec 2020 Kun Zhang, Le Wu, Guangyi Lv, Meng Wang, Enhong Chen, Shulan Ruan

Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences.

Relation Classification

Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction

no code implementations13 Dec 2020 Kai Zhang, Hao Qian, Qing Cui, Qi Liu, Longfei Li, Jun Zhou, Jianhui Ma, Enhong Chen

In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature.

Click-Through Rate Prediction

Sampling-Decomposable Generative Adversarial Recommender

no code implementations NeurIPS 2020 Binbin Jin, Defu Lian, Zheng Liu, Qi Liu, Jianhui Ma, Xing Xie, Enhong Chen

The GAN-style recommenders (i. e., IRGAN) addresses the challenge by learning a generator and a discriminator adversarially, such that the generator produces increasingly difficult samples for the discriminator to accelerate optimizing the discrimination objective.

Incorporating BERT into Parallel Sequence Decoding with Adapters

1 code implementation NeurIPS 2020 Junliang Guo, Zhirui Zhang, Linli Xu, Hao-Ran Wei, Boxing Chen, Enhong Chen

Our framework is based on a parallel sequence decoding algorithm named Mask-Predict considering the bi-directional and conditional independent nature of BERT, and can be adapted to traditional autoregressive decoding easily.

Machine Translation Natural Language Understanding +2

MMEA: Entity Alignment for Multi-Modal Knowledge Graphs

1 code implementation20 Aug 2020 Liyi Chen, Zhi Li, Yijun Wang, Tong Xu, Zhefeng Wang, Enhong Chen

To that end, in this paper, we propose a novel solution called Multi-Modal Entity Alignment (MMEA) to address the problem of entity alignment in a multi-modal view.

Knowledge Graphs Multimodal Deep Learning +1

Accuracy Prediction with Non-neural Model for Neural Architecture Search

1 code implementation9 Jul 2020 Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, Tie-Yan Liu

Considering that most architectures are represented as sequences of discrete symbols which are more like tabular data and preferred by non-neural predictors, in this paper, we study an alternative approach which uses non-neural model for accuracy prediction.

Neural Architecture Search

ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction

1 code implementation7 Jul 2020 Zhongkai Hao, Chengqiang Lu, Zheyuan Hu, Hao Wang, Zhenya Huang, Qi Liu, Enhong Chen, Cheekong Lee

Here we propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules.

Active Learning Molecular Property Prediction +1

Jointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation

no code implementations ACL 2020 Junliang Guo, Linli Xu, Enhong Chen

In this work, we introduce a jointly masked sequence-to-sequence model and explore its application on non-autoregressive neural machine translation{\textasciitilde}(NAT).

Language Modelling Machine Translation +1

Lightrec: A memory and search-efficient recommender system

1 code implementation International World Wide Web Conference 2020 Defu Lian, Haoyu Wang, Zheng Liu, Jianxun Lian, Enhong Chen, Xing Xie

On top of such a structure, LightRec will have an item represented as additive composition of B codewords, which are optimally selected from each of the codebooks.

Recommendation Systems

Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking

no code implementations5 Apr 2020 Shi Yin, Shangfei Wang, Xiaoping Chen, Enhong Chen

These 1D heatmaps reduce spatial complexity significantly compared to current heatmap regression methods, which use 2D heatmaps to represent the joint distributions of x and y coordinates.

Face Alignment Facial Landmark Detection +3

Domain Adaption for Knowledge Tracing

no code implementations14 Jan 2020 Song Cheng, Qi Liu, Enhong Chen

We refer to this problem as domain adaptation for knowledge tracing (DAKT) which contains two aspects: (1) how to achieve great knowledge tracing performance in each domain.

Domain Adaptation Knowledge Tracing

Deep Technology Tracing for High-tech Companies

no code implementations2 Jan 2020 Han Wu, Kun Zhang, Guangyi Lv, Qi Liu, Runlong Yu, Weihao Zhao, Enhong Chen, Jianhui Ma

Technological change and innovation are vitally important, especially for high-tech companies.

Vocal Bursts Intensity Prediction

Variance Reduced Local SGD with Lower Communication Complexity

1 code implementation30 Dec 2019 Xianfeng Liang, Shuheng Shen, Jingchang Liu, Zhen Pan, Enhong Chen, Yifei Cheng

To accelerate the training of machine learning models, distributed stochastic gradient descent (SGD) and its variants have been widely adopted, which apply multiple workers in parallel to speed up training.

BIG-bench Machine Learning

Estimating Early Fundraising Performance of Innovations via Graph-based Market Environment Model

no code implementations14 Dec 2019 Likang Wu, Zhi Li, Hongke Zhao, Zhen Pan, Qi Liu, Enhong Chen

In the crowdfunding market, the early fundraising performance of the project is a concerned issue for both creators and platforms.

Efficient Pure Exploration in Adaptive Round model

1 code implementation NeurIPS 2019 Tianyuan Jin, Jieming Shi, Xiaokui Xiao, Enhong Chen

For PAC problem, we achieve optimal query complexity and use only $O(\log_{\frac{k}{\delta}}^*(n))$ rounds, which matches the lower bound of round complexity, while most of existing works need $\Theta(\log \frac{n}{k})$ rounds.

A Machine Learning-enhanced Robust P-Phase Picker for Real-time Seismic Monitoring

no code implementations21 Nov 2019 Dazhong Shen, Qi Zhang, Tong Xu, HengShu Zhu, Wenjia Zhao, Zikai Yin, Peilun Zhou, Lihua Fang, Enhong Chen, Hui Xiong

To this end, in this paper, we present a machine learning-enhanced framework based on ensemble learning strategy, EL-Picker, for the automatic identification of seismic P-phase arrivals on continuous and massive waveforms.

BIG-bench Machine Learning Ensemble Learning

Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation

2 code implementations20 Nov 2019 Junliang Guo, Xu Tan, Linli Xu, Tao Qin, Enhong Chen, Tie-Yan Liu

Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models.

Machine Translation Translation

Long-term Joint Scheduling for Urban Traffic

1 code implementation27 Oct 2019 Xianfeng Liang, Likang Wu, Joya Chen, Yang Liu, Runlong Yu, Min Hou, Han Wu, Yuyang Ye, Qi Liu, Enhong Chen

Recently, the traffic congestion in modern cities has become a growing worry for the residents.


Balanced One-shot Neural Architecture Optimization

1 code implementation24 Sep 2019 Renqian Luo, Tao Qin, Enhong Chen

One-shot NAS is proposed to reduce the expense but shows inferior performance against conventional NAS and is not adequately stable.

Neural Architecture Search Test

Is Heuristic Sampling Necessary in Training Deep Object Detectors?

13 code implementations11 Sep 2019 Joya Chen, Dong Liu, Tong Xu, Shiwei Wu, Yifei Cheng, Enhong Chen

In this paper, we challenge the necessity of such hard/soft sampling methods for training accurate deep object detectors.

General Classification Instance Segmentation +1

STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control

no code implementations28 Aug 2019 Yanan Wang, Tong Xu, Xin Niu, Chang Tan, Enhong Chen, Hui Xiong

Moreover, based on the temporally-dependent traffic information, we design a Graph Neural Network based model to represent relationships among multiple traffic lights, and the decision for each traffic light will be made in a distributed way by the deep Q-learning method.

Management Multi-agent Reinforcement Learning +1

Residual Objectness for Imbalance Reduction

no code implementations24 Aug 2019 Joya Chen, Dong Liu, Bin Luo, Xuezheng Peng, Tong Xu, Enhong Chen

For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds.

Neural Cognitive Diagnosis for Intelligent Education Systems

1 code implementation23 Aug 2019 Fei Wang, Qi Liu, Enhong Chen, Zhenya Huang, Yuying Chen, Yu Yin, Zai Huang, Shijin Wang

Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts.

EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction

1 code implementation7 Jun 2019 Qi Liu, Zhenya Huang, Yu Yin, Enhong Chen, Hui Xiong, Yu Su, Guoping Hu

In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise's content.

Knowledge Tracing

Chinese Embedding via Stroke and Glyph Information: A Dual-channel View

no code implementations3 Jun 2019 Hanqing Tao, Shiwei Tong, Tong Xu, Qi Liu, Enhong Chen

Recent studies have consistently given positive hints that morphology is helpful in enriching word embeddings.

Word Embeddings Word Similarity

Budgeted Policy Learning for Task-Oriented Dialogue Systems

no code implementations ACL 2019 Zhirui Zhang, Xiujun Li, Jianfeng Gao, Enhong Chen

This paper presents a new approach that extends Deep Dyna-Q (DDQ) by incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed, small amount of user interactions (budget) for learning task-oriented dialogue agents.

Scheduling Task-Oriented Dialogue Systems

Promotion of Answer Value Measurement with Domain Effects in Community Question Answering Systems

no code implementations1 Jun 2019 Binbin Jin, Enhong Chen, Hongke Zhao, Zhenya Huang, Qi Liu, HengShu Zhu, Shui Yu

Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (Q&A), where the multi-facet domain effects in CQA are still underexplored.

Answer Selection Community Question Answering

Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach

no code implementations30 May 2019 Min Hou, Le Wu, Enhong Chen, Zhi Li, Vincent W. Zheng, Qi Liu

When making cloth decisions, people usually show preferences for different semantic attributes (e. g., the clothes with v-neck collar).

Ranked #2 on Recommendation Systems on Amazon Fashion (using extra training data)

Recommendation Systems

QuesNet: A Unified Representation for Heterogeneous Test Questions

no code implementations27 May 2019 Yu Yin, Qi Liu, Zhenya Huang, Enhong Chen, Wei Tong, Shijin Wang, Yu Su

Then we propose a two-level hierarchical pre-training algorithm to learn better understanding of test questions in an unsupervised way.

Language Modelling Test

Transcribing Content from Structural Images with Spotlight Mechanism

no code implementations27 May 2019 Yu Yin, Zhenya Huang, Enhong Chen, Qi Liu, Fuzheng Zhang, Xing Xie, Guoping Hu

Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly.

MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network

no code implementations27 May 2019 Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, Wen Su

Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention.

Multi-Task Learning Representation Learning

Exploiting Cognitive Structure for Adaptive Learning

no code implementations23 May 2019 Qi Liu, Shiwei Tong, Chuanren Liu, Hongke Zhao, Enhong Chen, Haiping Ma, Shijin Wang

Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e. g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items.

Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach

no code implementations21 Dec 2018 Chuan Qin, HengShu Zhu, Tong Xu, Chen Zhu, Liang Jiang, Enhong Chen, Hui Xiong

The wide spread use of online recruitment services has led to information explosion in the job market.

Bidirectional Generative Adversarial Networks for Neural Machine Translation

no code implementations CONLL 2018 Zhirui Zhang, Shujie Liu, Mu Li, Ming Zhou, Enhong Chen

To address this issue and stabilize the GAN training, in this paper, we propose a novel Bidirectional Generative Adversarial Network for Neural Machine Translation (BGAN-NMT), which aims to introduce a generator model to act as the discriminator, whereby the discriminator naturally considers the entire translation space so that the inadequate training problem can be alleviated.

Language Modelling Machine Translation +2

Style Transfer as Unsupervised Machine Translation

no code implementations23 Aug 2018 Zhirui Zhang, Shuo Ren, Shujie Liu, Jianyong Wang, Peng Chen, Mu Li, Ming Zhou, Enhong Chen

Language style transferring rephrases text with specific stylistic attributes while preserving the original attribute-independent content.

NMT Style Transfer +2

Neural Architecture Optimization

5 code implementations NeurIPS 2018 Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, Tie-Yan Liu

The performance predictor and the encoder enable us to perform gradient based optimization in the continuous space to find the embedding of a new architecture with potentially better accuracy.

Evolutionary Algorithms General Classification +4

Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions

no code implementations ICLR 2019 Zaiyi Chen, Zhuoning Yuan, Jin-Feng Yi, Bo-Wen Zhou, Enhong Chen, Tianbao Yang

For example, there is still a lack of theories of convergence for SGD and its variants that use stagewise step size and return an averaged solution in practice.

Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors

no code implementations3 Aug 2018 Zhi Li, Hongke Zhao, Qi Liu, Zhenya Huang, Tao Mei, Enhong Chen

In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations.

Session-Based Recommendations

SADAGRAD: Strongly Adaptive Stochastic Gradient Methods

no code implementations ICML 2018 Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang

Although the convergence rates of existing variants of ADAGRAD have a better dependence on the number of iterations under the strong convexity condition, their iteration complexities have a explicitly linear dependence on the dimensionality of the problem.

Joint Training for Neural Machine Translation Models with Monolingual Data

no code implementations1 Mar 2018 Zhirui Zhang, Shujie Liu, Mu Li, Ming Zhou, Enhong Chen

Monolingual data have been demonstrated to be helpful in improving translation quality of both statistical machine translation (SMT) systems and neural machine translation (NMT) systems, especially in resource-poor or domain adaptation tasks where parallel data are not rich enough.

Domain Adaptation Machine Translation +2

Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective

2 code implementations11 Nov 2017 Junliang Guo, Linli Xu, Xunpeng Huang, Enhong Chen

In this paper, we take a matrix factorization perspective of network embedding, and incorporate structure, content and label information of the network simultaneously.

Link Prediction Network Embedding +1

Finding Theme Communities from Database Networks

no code implementations23 Sep 2017 Lingyang Chu, Zhefeng Wang, Jian Pei, Yanyan Zhang, Yu Yang, Enhong Chen

Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities.

Stack-based Multi-layer Attention for Transition-based Dependency Parsing

no code implementations EMNLP 2017 Zhirui Zhang, Shujie Liu, Mu Li, Ming Zhou, Enhong Chen

Although sequence-to-sequence (seq2seq) network has achieved significant success in many NLP tasks such as machine translation and text summarization, simply applying this approach to transition-based dependency parsing cannot yield a comparable performance gain as in other state-of-the-art methods, such as stack-LSTM and head selection.

Language Modelling Machine Translation +3

Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph

no code implementations19 May 2015 Fei Tian, Bin Gao, Enhong Chen, Tie-Yan Liu

Although these works have achieved certain success, they have neglected some important facts about knowledge graphs: (i) many relationships in knowledge graphs are \emph{many-to-one}, \emph{one-to-many} or even \emph{many-to-many}, rather than simply \emph{one-to-one}; (ii) most head entities and tail entities in knowledge graphs come from very different semantic spaces.

Knowledge Graphs

Agent Behavior Prediction and Its Generalization Analysis

no code implementations19 Apr 2014 Fei Tian, Haifang Li, Wei Chen, Tao Qin, Enhong Chen, Tie-Yan Liu

Then we prove a generalization bound for the machine learning algorithms on the behavior data generated by the new Markov chain, which depends on both the Markovian parameters and the covering number of the function class compounded by the loss function for behavior prediction and the behavior prediction model.

BIG-bench Machine Learning

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