Search Results for author: Hong Chen

Found 99 papers, 31 papers with code

Improving Privacy Guarantee and Efficiency of Latent Dirichlet Allocation Model Training Under Differential Privacy

no code implementations Findings (EMNLP) 2021 Tao Huang, Hong Chen

To improve the privacy guarantee and efficiency, we combine a subsampling method with CGS and propose a novel LDA training algorithm with differential privacy, SUB-LDA.

P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion

no code implementations Findings (EMNLP) 2021 Jingwen Xu, Jing Zhang, Xirui Ke, Yuxiao Dong, Hong Chen, Cuiping Li, Yongbin Liu

Its general process is to first encode the implicit relation of an entity pair and then match the relation of a query entity pair with the relations of the reference entity pairs.

Knowledge Graph Completion Relation

Sparse Shrunk Additive Models

no code implementations ICML 2020 Hong Chen, Guodong Liu, Heng Huang

Meanwhile, in these feature selection models, the interactions between features are often ignored or just discussed under prior structure information.

Additive models feature selection +1

LLMTune: Accelerate Database Knob Tuning with Large Language Models

no code implementations17 Apr 2024 Xinmei Huang, Haoyang Li, Jing Zhang, Xinxin Zhao, Zhiming Yao, Yiyan Li, Zhuohao Yu, Tieying Zhang, Hong Chen, Cuiping Li

Database knob tuning is a critical challenge in the database community, aiming to optimize knob values to enhance database performance for specific workloads.

Language Modelling Large Language Model

TextCoT: Zoom In for Enhanced Multimodal Text-Rich Image Understanding

1 code implementation15 Apr 2024 Bozhi Luan, Hao Feng, Hong Chen, Yonghui Wang, Wengang Zhou, Houqiang Li

The image overview stage provides a comprehensive understanding of the global scene information, and the coarse localization stage approximates the image area containing the answer based on the question asked.

Question Answering Visual Question Answering (VQA)

SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation

1 code implementation2 Apr 2024 Shasha Guo, Lizi Liao, Jing Zhang, Yanling Wang, Cuiping Li, Hong Chen

Knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB.

Question Generation Question-Generation

Negative Label Guided OOD Detection with Pretrained Vision-Language Models

1 code implementation29 Mar 2024 Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han

In this paper, we propose a novel post hoc OOD detection method, called NegLabel, which takes a vast number of negative labels from extensive corpus databases.

Out of Distribution (OOD) Detection

Open-World Semi-Supervised Learning for Node Classification

1 code implementation18 Mar 2024 Yanling Wang, Jing Zhang, Lingxi Zhang, Lixin Liu, Yuxiao Dong, Cuiping Li, Hong Chen, Hongzhi Yin

Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph community.

Classification Contrastive Learning +2

Event-Driven Learning for Spiking Neural Networks

no code implementations1 Mar 2024 Wenjie Wei, Malu Zhang, Jilin Zhang, Ammar Belatreche, Jibin Wu, Zijing Xu, Xuerui Qiu, Hong Chen, Yang Yang, Haizhou Li

Specifically, we introduce two novel event-driven learning methods: the spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent event-driven (MPD-ED) algorithms.

Online Ecological Gearshift Strategy via Neural Network with Soft-Argmax Operator

no code implementations28 Feb 2024 Xi Luo, Shiying Dong, Jinlong Hong, Bingzhao Gao, Hong Chen

This paper presents a neural network optimizer with soft-argmax operator to achieve an ecological gearshift strategy in real-time.

Model Predictive Control

CodeS: Towards Building Open-source Language Models for Text-to-SQL

1 code implementation26 Feb 2024 Haoyang Li, Jing Zhang, Hanbing Liu, Ju Fan, Xiaokang Zhang, Jun Zhu, Renjie Wei, Hongyan Pan, Cuiping Li, Hong Chen

To address the limitations, we introduce CodeS, a series of pre-trained language models with parameters ranging from 1B to 15B, specifically designed for the text-to-SQL task.

Data Augmentation Domain Adaptation +2

Evolutionary Reinforcement Learning: A Systematic Review and Future Directions

no code implementations20 Feb 2024 Yuanguo Lin, Fan Lin, Guorong Cai, Hong Chen, Lixin Zou, Pengcheng Wu

In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution.

Adversarial Robustness Evolutionary Algorithms +2

DB-LLM: Accurate Dual-Binarization for Efficient LLMs

no code implementations19 Feb 2024 Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, DaCheng Tao

Large language models (LLMs) have significantly advanced the field of natural language processing, while the expensive memory and computation consumption impede their practical deployment.

Binarization Computational Efficiency +1

Spiking-PhysFormer: Camera-Based Remote Photoplethysmography with Parallel Spike-driven Transformer

no code implementations7 Feb 2024 Mingxuan Liu, Jiankai Tang, Haoxiang Li, Jiahao Qi, Siwei Li, Kegang Wang, Yuntao Wang, Hong Chen

Additionally, the power consumption of the transformer block is reduced by a factor of 12. 2, while maintaining decent performance as PhysFormer and other ANN-based models.

Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos

no code implementations28 Dec 2023 Houlun Chen, Xin Wang, Hong Chen, Zihan Song, Jia Jia, Wenwu Zhu

To tackle these challenges, in this work we propose a Grounding-Prompter method, which is capable of conducting TSG in long videos through prompting LLM with multimodal information.

Denoising In-Context Learning +3

LLM4VG: Large Language Models Evaluation for Video Grounding

no code implementations21 Dec 2023 Wei Feng, Xin Wang, Hong Chen, Zeyang Zhang, Zihan Song, Yuwei Zhou, Wenwu Zhu

Recently, researchers have attempted to investigate the capability of LLMs in handling videos and proposed several video LLM models.

Image Captioning Video Grounding +1

FOSS: A Self-Learned Doctor for Query Optimizer

no code implementations11 Dec 2023 Kai Zhong, Luming Sun, Tao Ji, Cuiping Li, Hong Chen

They either learn to construct plans from scratch in a bottom-up manner or guide the plan generation behavior of traditional optimizer using hints.

PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance

no code implementations6 Dec 2023 Haichao Sha, Ruixuan Liu, Yixuan Liu, Hong Chen

We prove that pre-projection enhances the convergence of DP-SGD by reducing the dependence of clipping error and bias to a fraction of the top gradient eigenspace, and in theory, limits cross-client variance to improve the convergence under heterogeneous federation.

Federated Learning

VTimeLLM: Empower LLM to Grasp Video Moments

1 code implementation30 Nov 2023 Bin Huang, Xin Wang, Hong Chen, Zihan Song, Wenwu Zhu

Large language models (LLMs) have shown remarkable text understanding capabilities, which have been extended as Video LLMs to handle video data for comprehending visual details.

Dense Video Captioning Video-based Generative Performance Benchmarking (Consistency) +5

Post-training Quantization with Progressive Calibration and Activation Relaxing for Text-to-Image Diffusion Models

no code implementations10 Nov 2023 Siao Tang, Xin Wang, Hong Chen, Chaoyu Guan, Zewen Wu, Yansong Tang, Wenwu Zhu

In this paper, we propose a novel post-training quantization method PCR (Progressive Calibration and Relaxing) for text-to-image diffusion models, which consists of a progressive calibration strategy that considers the accumulated quantization error across timesteps, and an activation relaxing strategy that improves the performance with negligible cost.


Lightweight Diffusion Models with Distillation-Based Block Neural Architecture Search

no code implementations8 Nov 2023 Siao Tang, Xin Wang, Hong Chen, Chaoyu Guan, Yansong Tang, Wenwu Zhu

When retraining the searched architecture, we adopt a dynamic joint loss to maintain the consistency between supernet training and subnet retraining, which also provides informative objectives for each block and shortens the paths of gradient propagation.

Neural Architecture Search

VideoDreamer: Customized Multi-Subject Text-to-Video Generation with Disen-Mix Finetuning

no code implementations2 Nov 2023 Hong Chen, Xin Wang, Guanning Zeng, YiPeng Zhang, Yuwei Zhou, Feilin Han, Wenwu Zhu

The video generator is further customized for the given multiple subjects by the proposed Disen-Mix Finetuning and Human-in-the-Loop Re-finetuning strategy, which can tackle the attribute binding problem of multi-subject generation.

Attribute Text-to-Video Generation +1

Data-Centric Financial Large Language Models

no code implementations7 Oct 2023 Zhixuan Chu, Huaiyu Guo, Xinyuan Zhou, Yijia Wang, Fei Yu, Hong Chen, Wanqing Xu, Xin Lu, Qing Cui, Longfei Li, Jun Zhou, Sheng Li

Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance.

Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation

no code implementations29 Sep 2023 Zhen Liu, Hang Gao, Hao Ma, Shuo Cai, Yunfeng Hu, Ting Qu, Hong Chen, Xun Gong

Autonomous vehicle (AV) evaluation has been the subject of increased interest in recent years both in industry and in academia.

Reinforcement Learning (RL)

Diversifying Question Generation over Knowledge Base via External Natural Questions

no code implementations23 Sep 2023 Shasha Guo, Jing Zhang, Xirui Ke, Cuiping Li, Hong Chen

The above insights make diversifying question generation an intriguing task, where the first challenge is evaluation metrics for diversity.

Natural Questions Question Answering +2

A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining

no code implementations9 Sep 2023 Yuanguo Lin, Hong Chen, Wei Xia, Fan Lin, Zongyue Wang, Yong liu

With the increasing complexity and diversity of educational data, Deep Learning techniques have shown significant advantages in addressing the challenges associated with analyzing and modeling this data.

Knowledge Tracing

SAM-Deblur: Let Segment Anything Boost Image Deblurring

1 code implementation5 Sep 2023 Siwei Li, Mingxuan Liu, Yating Zhang, Shu Chen, Haoxiang Li, Zifei Dou, Hong Chen

Image deblurring is a critical task in the field of image restoration, aiming to eliminate blurring artifacts.

Deblurring Image Deblurring +1

Transformer Compression via Subspace Projection

no code implementations31 Aug 2023 Yuxuan Hu, Jing Zhang, Chen Zhao, Cuiping Li, Hong Chen

By projecting the whole transform model into a subspace, we enable matrix operations between the weight matrices in the model and features in a reduced-dimensional space, leading to significant reductions in model parameters and computing resources.

A Mobile Data-Driven Hierarchical Deep Reinforcement Learning Approach for Real-time Demand-Responsive Railway Rescheduling and Station Overcrowding Mitigation

no code implementations23 Aug 2023 Enze Liu, Zhiyuan Lin, Judith Y. T. Wang, Hong Chen

The use of MD has enabled the modelling of passenger dynamics in response to train delays and station crowdedness, and a real-time optimisation for rescheduling of train services in view of the change in demand as a result of passengers' behavioural response to disruption.

Semi-Supervised Learning via Weight-aware Distillation under Class Distribution Mismatch

1 code implementation ICCV 2023 Pan Du, Suyun Zhao, Zisen Sheng, Cuiping Li, Hong Chen

Specifically, WAD captures adaptive weights and high-quality pseudo labels to target instances by exploring point mutual information (PMI) in representation space to maximize the role of unlabeled data and filter unknown categories.

Mathematical Reasoning

Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks

1 code implementation20 Aug 2023 Mingxuan Liu, Jie Gan, Rui Wen, Tao Li, Yongli Chen, Hong Chen

To fill the gap, we propose a Spiking-Diffusion model, which is based on the vector quantized discrete diffusion model.

Image Generation

ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer

no code implementations22 May 2023 Huadai Liu, Rongjie Huang, Xuan Lin, Wenqiang Xu, Maozong Zheng, Hong Chen, Jinzheng He, Zhou Zhao

To mitigate the data scarcity in learning visual acoustic information, we 1) introduce a self-supervised learning framework to enhance both the visual-text encoder and denoiser decoder; 2) leverage the diffusion transformer scalable in terms of parameters and capacity to learn visual scene information.

Denoising Self-Supervised Learning

CageViT: Convolutional Activation Guided Efficient Vision Transformer

no code implementations17 May 2023 Hao Zheng, Jinbao Wang, XianTong Zhen, Hong Chen, Jingkuan Song, Feng Zheng

Recently, Transformers have emerged as the go-to architecture for both vision and language modeling tasks, but their computational efficiency is limited by the length of the input sequence.

Computational Efficiency Image Classification +1

DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image Generation

1 code implementation5 May 2023 Hong Chen, YiPeng Zhang, Simin Wu, Xin Wang, Xuguang Duan, Yuwei Zhou, Wenwu Zhu

To tackle the problems, we propose DisenBooth, an identity-preserving disentangled tuning framework for subject-driven text-to-image generation.

Denoising Disentanglement +1

DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction

no code implementations3 May 2023 Chen Zhu, Liang Du, Hong Chen, Shuang Zhao, Zixun Sun, Xin Wang, Wenwu Zhu

To tackle this problem, inspired by the Global Workspace Theory in conscious processing, which posits that only a specific subset of the product features are pertinent while the rest can be noisy and even detrimental to human-click behaviors, we propose a CTR model that enables Dynamic Embedding Learning with Truncated Conscious Attention for CTR prediction, termed DELTA.

Click-Through Rate Prediction

Understanding the Generalization Ability of Deep Learning Algorithms: A Kernelized Renyi's Entropy Perspective

1 code implementation2 May 2023 Yuxin Dong, Tieliang Gong, Hong Chen, Chen Li

However, the current generalization error bounds within this framework are still far from optimal, while substantial improvements on these bounds are quite challenging due to the intractability of high-dimensional information quantities.

Detecting Out-of-distribution Data through In-distribution Class Prior

1 code implementation ICML 2023 Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han

In this paper, we show that this assumption makes the above methods incapable when the ID model is trained with class-imbalanced data. Fortunately, by analyzing the causal relations between ID/OOD classes and features, we identify several common scenarios where the OOD-to-ID probabilities should be the ID-class-prior distribution and propose two strategies to modify existing inference-time detection methods: 1) replace the uniform distribution with the ID-class-prior distribution if they explicitly use the uniform distribution; 2) otherwise, reweight their scores according to the similarity between the ID-class-prior distribution and the softmax outputs of the pre-trained model.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

no code implementations11 Apr 2023 Yixuan Liu, Suyun Zhao, Li Xiong, YuHan Liu, Hong Chen

In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model.

Federated Learning

DIRE for Diffusion-Generated Image Detection

1 code implementation ICCV 2023 Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, Houqiang Li

We find that existing detectors struggle to detect images generated by diffusion models, even if we include generated images from a specific diffusion model in their training data.

On the Stability and Generalization of Triplet Learning

no code implementations20 Feb 2023 Jun Chen, Hong Chen, Xue Jiang, Bin Gu, Weifu Li, Tieliang Gong, Feng Zheng

Triplet learning, i. e. learning from triplet data, has attracted much attention in computer vision tasks with an extremely large number of categories, e. g., face recognition and person re-identification.

Face Recognition Metric Learning +1

Stability-based Generalization Analysis for Mixtures of Pointwise and Pairwise Learning

no code implementations20 Feb 2023 Jiahuan Wang, Jun Chen, Hong Chen, Bin Gu, Weifu Li, Xin Tang

Recently, some mixture algorithms of pointwise and pairwise learning (PPL) have been formulated by employing the hybrid error metric of "pointwise loss + pairwise loss" and have shown empirical effectiveness on feature selection, ranking and recommendation tasks.

feature selection Generalization Bounds +1

RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL

1 code implementation12 Feb 2023 Haoyang Li, Jing Zhang, Cuiping Li, Hong Chen

Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i. e., tables and columns) and the skeleton (i. e., SQL keywords).

Language Modelling Semantic Parsing +2

Temporal-Coded Spiking Neural Networks with Dynamic Firing Threshold: Learning with Event-Driven Backpropagation

no code implementations ICCV 2023 Wenjie Wei, Malu Zhang, Hong Qu, Ammar Belatreche, Jian Zhang, Hong Chen

As a temporal encoding scheme for SNNs, Time-To-First-Spike (TTFS) encodes information using the timing of a single spike, which allows spiking neurons to transmit information through sparse spike trains and results in lower power consumption and higher computational efficiency compared to traditional rate-based encoding counterparts.

Computational Efficiency Image Classification

Superclass Learning With Representation Enhancement

no code implementations CVPR 2023 Jinlong Kang, Liyuan Shang, Suyun Zhao, Hong Chen, Cuiping Li, Zeyu Gan

In many real scenarios, data are often divided into a handful of artificial super categories in terms of expert knowledge rather than the representations of images.

On the Global Solution of Soft k-Means

no code implementations7 Dec 2022 Feiping Nie, Hong Chen, Rong Wang, Xuelong Li

This paper presents an algorithm to solve the Soft k-Means problem globally.


Robust and Fast Measure of Information via Low-rank Representation

1 code implementation30 Nov 2022 Yuxin Dong, Tieliang Gong, Shujian Yu, Hong Chen, Chen Li

The matrix-based R\'enyi's entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution.

Computational Efficiency

Disentangled Representation Learning

no code implementations21 Nov 2022 Xin Wang, Hong Chen, Si'ao Tang, Zihao Wu, Wenwu Zhu

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation form.

Representation Learning

Oracle-guided Contrastive Clustering

no code implementations1 Nov 2022 Mengdie Wang, Liyuan Shang, Suyun Zhao, Yiming Wang, Hong Chen, Cuiping Li, XiZhao Wang

Accordingly, the query results, guided by oracles with distinctive demands, may drive the OCC's clustering results in a desired orientation.

Active Learning Clustering +2

Character-Centric Story Visualization via Visual Planning and Token Alignment

2 code implementations16 Oct 2022 Hong Chen, Rujun Han, Te-Lin Wu, Hideki Nakayama, Nanyun Peng

This task requires machines to 1) understand long text inputs and 2) produce a globally consistent image sequence that illustrates the contents of the story.

Story Visualization Text-to-Image Generation

StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning

1 code implementation16 Oct 2022 Hong Chen, Duc Minh Vo, Hiroya Takamura, Yusuke Miyao, Hideki Nakayama

Existing automatic story evaluation methods place a premium on story lexical level coherence, deviating from human preference.

Comment Generation

Global Weighted Tensor Nuclear Norm for Tensor Robust Principal Component Analysis

no code implementations28 Sep 2022 Libin Wang, Yulong Wang, Shiyuan Wang, Youheng Liu, Yutao Hu, Longlong Chen, Hong Chen

Tensor Robust Principal Component Analysis (TRPCA), which aims to recover a low-rank tensor corrupted by sparse noise, has attracted much attention in many real applications.

NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results

no code implementations31 Aug 2022 Dustin Carrión-Ojeda, Hong Chen, Adrian El Baz, Sergio Escalera, Chaoyu Guan, Isabelle Guyon, Ihsan Ullah, Xin Wang, Wenwu Zhu

We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on "cross-domain" meta-learning.

Few-Shot Image Classification Few-Shot Learning +1

Bag of Tricks for Out-of-Distribution Generalization

no code implementations23 Aug 2022 Zining Chen, Weiqiu Wang, Zhicheng Zhao, Aidong Men, Hong Chen

Recently, out-of-distribution (OOD) generalization has attracted attention to the robustness and generalization ability of deep learning based models, and accordingly, many strategies have been made to address different aspects related to this issue.

Domain Generalization Out-of-Distribution Generalization

AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments

no code implementations20 Aug 2022 Yang Zhao, Wenqiang Xu, Xuan Lin, Jingjing Huo, Hong Chen, Zhou Zhao

The task of argument mining aims to detect all possible argumentative components and identify their relationships automatically.

Argument Mining

OMG: Observe Multiple Granularities for Natural Language-Based Vehicle Retrieval

1 code implementation18 Apr 2022 Yunhao Du, Binyu Zhang, Xiangning Ruan, Fei Su, Zhicheng Zhao, Hong Chen

For the textual representation, one global embedding, three local embeddings and a color-type prompt embedding are extracted to represent various granularities of semantic features.


PrivateRec: Differentially Private Training and Serving for Federated News Recommendation

no code implementations18 Apr 2022 Ruixuan Liu, Yanlin Wang, Yang Cao, Lingjuan Lyu, Weike Pan, Yun Chen, Hong Chen

Collecting and training over sensitive personal data raise severe privacy concerns in personalized recommendation systems, and federated learning can potentially alleviate the problem by training models over decentralized user data. However, a theoretically private solution in both the training and serving stages of federated recommendation is essential but still lacking. Furthermore, naively applying differential privacy (DP) to the two stages in federated recommendation would fail to achieve a satisfactory trade-off between privacy and utility due to the high-dimensional characteristics of model gradients and hidden representations. In this work, we propose a federated news recommendation method for achieving a better utility in model training and online serving under a DP guarantee. We first clarify the DP definition over behavior data for each round in the life-circle of federated recommendation systems. Next, we propose a privacy-preserving online serving mechanism under this definition based on the idea of decomposing user embeddings with public basic vectors and perturbing the lower-dimensional combination coefficients.

Federated Learning News Recommendation +2

NOC-REK: Novel Object Captioning with Retrieved Vocabulary from External Knowledge

no code implementations CVPR 2022 Duc Minh Vo, Hong Chen, Akihiro Sugimoto, Hideki Nakayama

We propose an end-to-end Novel Object Captioning with Retrieved vocabulary from External Knowledge method (NOC-REK), which simultaneously learns vocabulary retrieval and caption generation, successfully describing novel objects outside of the training dataset.

Caption Generation Object +3

Error-based Knockoffs Inference for Controlled Feature Selection

no code implementations9 Mar 2022 Xuebin Zhao, Hong Chen, Yingjie Wang, Weifu Li, Tieliang Gong, Yulong Wang, Feng Zheng

Recently, the scheme of model-X knockoffs was proposed as a promising solution to address controlled feature selection under high-dimensional finite-sample settings.

Feature Importance feature selection

No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices

no code implementations16 Feb 2022 Ruixuan Liu, Fangzhao Wu, Chuhan Wu, Yanlin Wang, Lingjuan Lyu, Hong Chen, Xing Xie

In this way, all the clients can participate in the model learning in FL, and the final model can be big and powerful enough.

Federated Learning Knowledge Distillation +1

Equilibrium Defaultable Corporate Debt and Investment

no code implementations11 Feb 2022 Hong Chen, Murray Zed Frank

In dynamic capital structure models with an investor break-even condition, the firm's Bellman equation may not generate a contraction mapping, so the standard existence and uniqueness conditions do not apply.

An Accelerator for Rule Induction in Fuzzy Rough Theory

1 code implementation7 Jan 2022 Suyun Zhao, Zhigang Dai, XiZhao Wang, Peng Ni, Hengheng Luo, Hong Chen, Cuiping Li

First, a rule induction method based on consistence degree, called Consistence-based Value Reduction (CVR), is proposed and used as basis to accelerate.

Explainable artificial intelligence

Markov subsampling based Huber Criterion

no code implementations12 Dec 2021 Tieliang Gong, Yuxin Dong, Hong Chen, Bo Dong, Chen Li

Subsampling is an important technique to tackle the computational challenges brought by big data.

Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models

1 code implementation12 Dec 2021 Yu Feng, Jing Zhang, Xiaokang Zhang, Lemao Liu, Cuiping Li, Hong Chen

Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions.

Data Augmentation Knowledge Base Question Answering

Regularized Modal Regression on Markov-dependent Observations: A Theoretical Assessment

no code implementations9 Dec 2021 Tielang Gong, Yuxin Dong, Hong Chen, Bo Dong, Wei Feng, Chen Li

Our results show that the Markov dependence impacts on the generalization error in the way that sample size would be discounted by a multiplicative factor depending on the spectral gap of underlying Markov chain.

Learning Theory regression

Self-supervised Graph Learning for Occasional Group Recommendation

no code implementations4 Dec 2021 Bowen Hao, Hongzhi Yin, Cuiping Li, Hong Chen

As each occasional group has extremely sparse interactions with items, traditional group recommendation methods can not learn high-quality group representations.

Contrastive Learning Graph Learning +3

A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation

no code implementations4 Dec 2021 Bowen Hao, Hongzhi Yin, Jing Zhang, Cuiping Li, Hong Chen

In terms of the pretext task, in addition to considering the intra-correlations of users and items by the embedding reconstruction task, we add embedding contrastive learning task to capture inter-correlations of users and items.

Contrastive Learning Meta-Learning +1

Curriculum Disentangled Recommendation with Noisy Multi-feedback

1 code implementation NeurIPS 2021 Hong Chen, Yudong Chen, Xin Wang, Ruobing Xie, Rui Wang, Feng Xia, Wenwu Zhu

However, learning such disentangled representations from multi-feedback data is challenging because i) multi-feedback is complex: there exist complex relations among different types of feedback (e. g., click, unclick, and dislike, etc) as well as various user intentions, and ii) multi-feedback is noisy: there exists noisy (useless) information both in features and labels, which may deteriorate the recommendation performance.

Denoising Representation Learning

Eco-Coasting Strategies Using Road Grade Preview: Evaluation and Online Implementation Based on Mixed Integer Model Predictive Control

no code implementations14 Nov 2021 Yongjun Yan, Nan Li, Jinlong Hong, Bingzhao Gao, Hong Chen, Jing Sun, Ziyou Song

However, the comprehensive comparison between different coasting strategies and online performance of the eco-coasting strategy using road grade preview are still unclear because of the oversimplification and the integer variable in the optimal control problems.

Model Predictive Control

SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation

no code implementations Findings (EMNLP) 2021 Hong Chen, Hiroya Takamura, Hideki Nakayama

Generating texts in scientific papers requires not only capturing the content contained within the given input but also frequently acquiring the external information called \textit{context}.

Text Generation

Huber Additive Models for Non-stationary Time Series Analysis

no code implementations ICLR 2022 Yingjie Wang, Xianrui Zhong, Fengxiang He, Hong Chen, DaCheng Tao

Moreover, the error bound for non-stationary time series contains a discrepancy measure for the shifts of the data distributions over time.

Additive models Causal Discovery +4

Commonsense Knowledge Aware Concept Selection For Diverse and Informative Visual Storytelling

no code implementations5 Feb 2021 Hong Chen, Yifei HUANG, Hiroya Takamura, Hideki Nakayama

To enrich the candidate concepts, a commonsense knowledge graph is created for each image sequence from which the concept candidates are proposed.

Informativeness Visual Storytelling

GraphPlan: Story Generation by Planning with Event Graph

no code implementations INLG (ACL) 2021 Hong Chen, Raphael Shu, Hiroya Takamura, Hideki Nakayama

In this paper, we focus on planning a sequence of events assisted by event graphs, and use the events to guide the generator.

Story Generation

A Bayesian Federated Learning Framework with Online Laplace Approximation

no code implementations3 Feb 2021 Liangxi Liu, Xi Jiang, Feng Zheng, Hong Chen, Guo-Jun Qi, Heng Huang, Ling Shao

On the client side, a prior loss that uses the global posterior probabilistic parameters delivered from the server is designed to guide the local training.

Federated Learning

Energy cost study for controlling complex social networks with conformity behavior

no code implementations11 Jan 2021 Hong Chen, Ee Hou Yong

Therefore, to understand controlling social systems with conformity, discrete-time modelling is used and the energy cost scaling laws are derived.

Physics and Society

Contrastive Coding for Active Learning Under Class Distribution Mismatch

no code implementations ICCV 2021 Pan Du, Suyun Zhao, Hui Chen, Shuwen Chai, Hong Chen, Cuiping Li

However, its performance deteriorates under class distribution mismatch, wherein the unlabeled data contain many samples out of the class distribution of labeled data.

Active Learning Contrastive Learning

Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach

1 code implementation28 Dec 2020 Bowen Hao, Jing Zhang, Cuiping Li, Hong Chen, Hongzhi Yin

On the one hand, the framework enables training multiple supervised ranking models upon the pseudo labels produced by multiple unsupervised ranking models.

CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking

2 code implementations14 Dec 2020 Bo Chen, Jing Zhang, Xiaokang Zhang, Xiaobin Tang, Lingfan Cai, Hong Chen, Cuiping Li, Peng Zhang, Jie Tang

In this paper, we propose CODE, which first pre-trains an expert linking model by contrastive learning on AMiner such that it can capture the representation and matching patterns of experts without supervised signals, then it is fine-tuned between AMiner and external sources to enhance the models transferability in an adversarial manner.

Active Learning Contrastive Learning +2

Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery

no code implementations NeurIPS 2020 Yingjie Wang, Hong Chen, Feng Zheng, Chen Xu, Tieliang Gong, Yanhong Chen

For high-dimensional observations in real environment, e. g., Coronal Mass Ejections (CMEs) data, the learning performance of previous methods may be degraded seriously due to the complex non-Gaussian noise and the insufficiency of prior knowledge on variable structure.

Additive models Bilevel Optimization +1

FLAME: Differentially Private Federated Learning in the Shuffle Model

1 code implementation17 Sep 2020 Ruixuan Liu, Yang Cao, Hong Chen, Ruoyang Guo, Masatoshi Yoshikawa

In this work, by leveraging the \textit{privacy amplification} effect in the recently proposed shuffle model of differential privacy, we achieve the best of two worlds, i. e., accuracy in the curator model and strong privacy without relying on any trusted party.

Federated Learning

FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection

no code implementations24 Mar 2020 Ruixuan Liu, Yang Cao, Masatoshi Yoshikawa, Hong Chen

To prevent privacy leakages from gradients that are calculated on users' sensitive data, local differential privacy (LDP) has been considered as a privacy guarantee in federated SGD recently.

Federated Learning

GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features

1 code implementation ICONIP 2019 2020 Hong Chen, Hisashi Koga

Specifically, it complements either the edge label information or the structural information which Graph2vec misses with the embeddings of the line graphs.

General Classification Graph Classification +1

Robust Learning-based Predictive Control for Discrete-time Nonlinear Systems with Unknown Dynamics and State Constraints

no code implementations22 Nov 2019 Xinglong Zhang, Jiahang Liu, Xin Xu, Shuyou Yu, Hong Chen

Robust model predictive control (MPC) is a well-known control technique for model-based control with constraints and uncertainties.

Model Predictive Control

Cascaded LSTMs based Deep Reinforcement Learning for Goal-driven Dialogue

1 code implementation31 Oct 2019 Yue Ma, Xiaojie Wang, Zhenjiang Dong, Hong Chen

Dialogue embeddings are learned by a LSTM at the middle of the network, and updated by the feeding of all turn embeddings.

Dialogue Management Management +3

Image Segmentation Based on Multiscale Fast Spectral Clustering

no code implementations12 Dec 2018 Chongyang Zhang, Guofeng Zhu, Minxin Chen, Hong Chen, Chenjian Wu

The computational complexity of Multiscale Fast Spectral Clustering is O(nlogn) and its memory cost is O(m).

Clustering Image Segmentation +3

Semantic Aware Attention Based Deep Object Co-segmentation

3 code implementations16 Oct 2018 Hong Chen, Yifei HUANG, Hideki Nakayama

Object co-segmentation is the task of segmenting the same objects from multiple images.

Object Segmentation

Group Sparse Additive Machine

no code implementations NeurIPS 2017 Hong Chen, Xiaoqian Wang, Cheng Deng, Heng Huang

Among them, learning models with grouped variables have shown competitive performance for prediction and variable selection.

Additive models Classification +2

Modal Regression based Atomic Representation for Robust Face Recognition

no code implementations5 Nov 2017 Yulong Wang, Yuan Yan Tang, Luoqing Li, Hong Chen

In this paper, we propose a modal regression based atomic representation and classification (MRARC) framework to alleviate such limitation.

Face Recognition General Classification +2

Error Analysis of Generalized Nyström Kernel Regression

no code implementations NeurIPS 2016 Hong Chen, Haifeng Xia, Heng Huang, Weidong Cai

Nystr\"{o}m method has been used successfully to improve the computational efficiency of kernel ridge regression (KRR).

Computational Efficiency regression

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