Search Results for author: Hui Liu

Found 121 papers, 62 papers with code

A Semantic Filter Based on Relations for Knowledge Graph Completion

no code implementations EMNLP 2021 Zongwei Liang, Junan Yang, Hui Liu, Keju Huang

experiments on the benchmark datasets show that the semantic filter based on relations can suppress the impact of other attribute dimensions and improve link prediction performance.

Attribute Knowledge Graph Embedding +2

Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning

no code implementations Findings (EMNLP) 2021 Zhan Shi, Hui Liu, Martin Renqiang Min, Christopher Malon, Li Erran Li, Xiaodan Zhu

Image captioning systems are expected to have the ability to combine individual concepts when describing scenes with concept combinations that are not observed during training.

Image Captioning Retrieval

SIFiD: Reassess Summary Factual Inconsistency Detection with LLM

no code implementations12 Mar 2024 Jiuding Yang, Hui Liu, Weidong Guo, Zhuwei Rao, Yu Xu, Di Niu

Ensuring factual consistency between the summary and the original document is paramount in summarization tasks.

Natural Language Inference Semantic Similarity +1

Predicting Single-cell Drug Sensitivity by Adaptive Weighted Feature for Adversarial Multi-source Domain Adaptation

no code implementations8 Mar 2024 Wei Duan, Hui Liu

The development of single-cell sequencing technology had promoted the generation of a large amount of single-cell transcriptional profiles, providing valuable opportunities to explore drug-resistant cell subpopulations in a tumor.

Domain Adaptation

Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images

no code implementations4 Mar 2024 Jianhan Qi, Yuheng Jia, Hui Liu, Junhui Hou

The state-of-the-art (SOTA) methods usually rely on superpixels, however, they do not fully utilize the spatial and spectral information in HSI 3-D structure, and their optimization targets are not clustering-oriented.

Clustering Contrastive Learning +1

Towards Calibrated Deep Clustering Network

no code implementations4 Mar 2024 Yuheng Jia, Jianhong Cheng, Hui Liu, Junhui Hou

Deep clustering has exhibited remarkable performance; however, the overconfidence problem, i. e., the estimated confidence for a sample belonging to a particular cluster greatly exceeds its actual prediction accuracy, has been overlooked in prior research.

Clustering Deep Clustering

Improving the JPEG-resistance of Adversarial Attacks on Face Recognition by Interpolation Smoothing

no code implementations26 Feb 2024 Kefu Guo, Fengfan Zhou, Hefei Ling, Ping Li, Hui Liu

JPEG compression can significantly impair the performance of adversarial face examples, which previous adversarial attacks on face recognition (FR) have not adequately addressed.

Adversarial Attack Face Recognition

Mixture of Link Predictors

no code implementations13 Feb 2024 Li Ma, Haoyu Han, Juanhui Li, Harry Shomer, Hui Liu, Xiaofeng Gao, Jiliang Tang

Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning.

Link Prediction

TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection

1 code implementation12 Feb 2024 Hui Liu, Wenya Wang, Haoru Li, Haoliang Li

The proliferation of fake news has emerged as a severe societal problem, raising significant interest from industry and academia.

Decision Making Fake News Detection

Copyright Protection in Generative AI: A Technical Perspective

no code implementations4 Feb 2024 Jie Ren, Han Xu, Pengfei He, Yingqian Cui, Shenglai Zeng, Jiankun Zhang, Hongzhi Wen, Jiayuan Ding, Hui Liu, Yi Chang, Jiliang Tang

We examine from two distinct viewpoints: the copyrights pertaining to the source data held by the data owners and those of the generative models maintained by the model builders.

Code-Based English Models Surprising Performance on Chinese QA Pair Extraction Task

no code implementations16 Jan 2024 Linghan Zheng, Hui Liu, Xiaojun Lin, Jiayuan Dong, Yue Sheng, Gang Shi, Zhiwei Liu, Hongwei Chen

In previous studies, code-based models have consistently outperformed text-based models in reasoning-intensive scenarios.

Retrieval

SVQ: Sparse Vector Quantization for Spatiotemporal Forecasting

1 code implementation6 Dec 2023 Chao Chen, Tian Zhou, Yanjun Zhao, Hui Liu, Liang Sun, Rong Jin

Moreover, we approximate the sparse regression process using a blend of a two-layer MLP and an extensive codebook.

Computational Efficiency Quantization +6

Robust Domain Misinformation Detection via Multi-modal Feature Alignment

1 code implementation24 Nov 2023 Hui Liu, Wenya Wang, Hao Sun, Anderson Rocha, Haoliang Li

We also propose a framework that simultaneously considers application scenarios of domain generalization (in which the target domain data is unavailable) and domain adaptation (in which unlabeled target domain data is available).

Domain Generalization Misinformation

Interpretable Modeling of Single-cell perturbation Responses to Novel Drugs Using Cycle Consistence Learning

no code implementations17 Nov 2023 Wei Huang, Aichun Zhu, Hui Liu

Phenotype-based screening has attracted much attention for identifying cell-active compounds.

Vision meets mmWave Radar: 3D Object Perception Benchmark for Autonomous Driving

no code implementations17 Nov 2023 Yizhou Wang, Jen-Hao Cheng, Jui-Te Huang, Sheng-Yao Kuan, Qiqian Fu, Chiming Ni, Shengyu Hao, Gaoang Wang, Guanbin Xing, Hui Liu, Jenq-Neng Hwang

This kind of radar format can enable machine learning models to generate more reliable object perception results after interacting and fusing the information or features between the camera and radar.

Autonomous Driving Sensor Fusion

Cross-domain feature disentanglement for interpretable modeling of tumor microenvironment impact on drug response

no code implementations15 Nov 2023 Jia Zhai, Hui Liu

This paper proposed a domain adaptation network for feature disentanglement to separate representations of cancer cells and TME of a tumor in patients.

Denoising Disentanglement +2

Global Structure-Aware Diffusion Process for Low-Light Image Enhancement

1 code implementation NeurIPS 2023 Jinhui Hou, Zhiyu Zhu, Junhui Hou, Hui Liu, Huanqiang Zeng, Hui Yuan

To harness the capabilities of diffusion models, we delve into this intricate process and advocate for the regularization of its inherent ODE-trajectory.

Low-Light Image Enhancement

Augment with Care: Enhancing Graph Contrastive Learning with Selective Spectrum Perturbation

no code implementations20 Oct 2023 Kaiqi Yang, Haoyu Han, Wei Jin, Hui Liu

Existing augmentation views with perturbed graph structures are usually based on random topology corruption in the spatial domain; however, from perspectives of the spectral domain, this approach may be ineffective as it fails to pose tailored impacts on the information of different frequencies, thus weakening the agreement between the augmentation views.

Contrastive Learning

Efficient and Effective Deep Multi-view Subspace Clustering

no code implementations15 Oct 2023 Yuxiu Lin, Hui Liu, Ren Wang, Qiang Guo, Caiming Zhang

i) The parameter scale of the FC layer is quadratic to sample numbers, resulting in high time and memory costs that significantly degrade their feasibility in large-scale datasets.

Clustering Computational Efficiency +1

Label-free Node Classification on Graphs with Large Language Models (LLMS)

1 code implementation7 Oct 2023 Zhikai Chen, Haitao Mao, Hongzhi Wen, Haoyu Han, Wei Jin, Haiyang Zhang, Hui Liu, Jiliang Tang

In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN.

Node Classification

FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models

no code implementations3 Oct 2023 Yingqian Cui, Jie Ren, Yuping Lin, Han Xu, Pengfei He, Yue Xing, Wenqi Fan, Hui Liu, Jiliang Tang

Text-to-image generative models based on latent diffusion models (LDM) have demonstrated their outstanding ability in generating high-quality and high-resolution images according to language prompt.

Face Transfer

On the Generalization of Training-based ChatGPT Detection Methods

1 code implementation2 Oct 2023 Han Xu, Jie Ren, Pengfei He, Shenglai Zeng, Yingqian Cui, Amy Liu, Hui Liu, Jiliang Tang

ChatGPT is one of the most popular language models which achieve amazing performance on various natural language tasks.

Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models

no code implementations22 Aug 2023 Zengxiang Li, Zhaoxiang Hou, Hui Liu, Ying Wang, Tongzhi Li, Longfei Xie, Chao Shi, Chengyi Yang, Weishan Zhang, Zelei Liu, Liang Xu

Preliminary experiments show that enterprises can enhance and accumulate intelligent capabilities through multimodal model federated learning, thereby jointly creating an smart city model that provides high-quality intelligent services covering energy infrastructure safety, residential community security, and urban operation management.

Federated Learning Management

Improving Text Semantic Similarity Modeling through a 3D Siamese Network

no code implementations18 Jul 2023 Jianxiang Zang, Hui Liu

Siamese networks have gained popularity as a method for modeling text semantic similarity.

Navigate Semantic Similarity +1

TVPR: Text-to-Video Person Retrieval and a New Benchmark

no code implementations14 Jul 2023 Fan Ni, Xu Zhang, Jianhui Wu, Guan-Nan Dong, Aichun Zhu, Hui Liu, Yue Zhang

To the best of our knowledge, TVPRN is the first successful attempt to use video for text-based person retrieval task and has achieved state-of-the-art performance on TVPReid dataset.

Person Retrieval Retrieval +3

Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

2 code implementations7 Jul 2023 Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, Jiliang Tang

The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding.

General Knowledge Node Classification

Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding Models

1 code implementation5 Jun 2023 Jiabang He, Yi Hu, Lei Wang, Xing Xu, Ning Liu, Hui Liu, Heng Tao Shen

Results from the experiments demonstrate that there is a significant performance gap between the in-distribution (ID) and OOD settings for document images, and that fine-grained analysis of distribution shifts can reveal the brittle nature of existing pre-trained VDU models and OOD generalization algorithms.

document understanding Question Answering

DiffusionShield: A Watermark for Copyright Protection against Generative Diffusion Models

no code implementations25 May 2023 Yingqian Cui, Jie Ren, Han Xu, Pengfei He, Hui Liu, Lichao Sun, Yue Xing, Jiliang Tang

By detecting the watermark from generated images, copyright infringement can be exposed with evidence.

Self-Explainable Graph Neural Networks for Link Prediction

no code implementations21 May 2023 Huaisheng Zhu, Dongsheng Luo, Xianfeng Tang, Junjie Xu, Hui Liu, Suhang Wang

Directly adopting existing post-hoc explainers for explaining link prediction is sub-optimal because: (i) post-hoc explainers usually adopt another strategy or model to explain a target model, which could misinterpret the target model; and (ii) GNN explainers for node classification identify crucial subgraphs around each node for the explanation; while for link prediction, one needs to explain the prediction for each pair of nodes based on graph structure and node attributes.

Link Prediction Node Classification

Interpretable Multimodal Misinformation Detection with Logic Reasoning

2 code implementations10 May 2023 Hui Liu, Wenya Wang, Haoliang Li

Multimodal misinformation on online social platforms is becoming a critical concern due to increasing credibility and easier dissemination brought by multimedia content, compared to traditional text-only information.

Misinformation

TCR: Short Video Title Generation and Cover Selection with Attention Refinement

no code implementations25 Apr 2023 Yakun Yu, Jiuding Yang, Weidong Guo, Hui Liu, Yu Xu, Di Niu

In this paper, we first collect and present a real-world dataset named Short Video Title Generation (SVTG) that contains videos with appealing titles and covers.

Video Captioning

Counterfactual Learning on Graphs: A Survey

1 code implementation3 Apr 2023 Zhimeng Guo, Teng Xiao, Zongyu Wu, Charu Aggarwal, Hui Liu, Suhang Wang

To facilitate the development of this promising direction, in this survey, we categorize and comprehensively review papers on graph counterfactual learning.

counterfactual Fairness +2

GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation

1 code implementation26 Mar 2023 Ji Qi, Jifan Yu, Teng Tu, Kunyu Gao, Yifan Xu, Xinyu Guan, Xiaozhi Wang, Yuxiao Dong, Bin Xu, Lei Hou, Juanzi Li, Jie Tang, Weidong Guo, Hui Liu, Yu Xu

Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i. e., long and informative commentary about the domain-specific scenes with appropriate reasoning) is still far from being solved, which however has great applications such as automatic sports narrative.

Video Captioning

ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction

1 code implementation ICCV 2023 Jiabang He, Lei Wang, Yi Hu, Ning Liu, Hui Liu, Xing Xu, Heng Tao Shen

To this end, we propose a simple but effective in-context learning framework called ICL-D3IE, which enables LLMs to perform DIE with different types of demonstration examples.

Document AI In-Context Learning

Models See Hallucinations: Evaluating the Factuality in Video Captioning

no code implementations6 Mar 2023 Hui Liu, Xiaojun Wan

In this work, we conduct a detailed human evaluation of the factuality in video captioning and collect two annotated factuality datasets.

Text Generation Video Captioning

Single-Cell Multimodal Prediction via Transformers

1 code implementation1 Mar 2023 Wenzhuo Tang, Hongzhi Wen, Renming Liu, Jiayuan Ding, Wei Jin, Yuying Xie, Hui Liu, Jiliang Tang

The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding of cellular states and dynamics.

Generative Diffusion Models on Graphs: Methods and Applications

1 code implementation6 Feb 2023 Chengyi Liu, Wenqi Fan, Yunqing Liu, Jiatong Li, Hang Li, Hui Liu, Jiliang Tang, Qing Li

Given the great success of diffusion models in image generation, increasing efforts have been made to leverage these techniques to advance graph generation in recent years.

Denoising Graph Generation +2

Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation

1 code implementation6 Feb 2023 Hongzhi Wen, Wenzhuo Tang, Wei Jin, Jiayuan Ding, Renming Liu, Xinnan Dai, Feng Shi, Lulu Shang, Hui Liu, Yuying Xie

In particular, investigate the following two key questions: (1) $\textit{how to encode spatial information of cells in transformers}$, and (2) $\textit{ how to train a transformer for transcriptomic imputation}$.

Computational Efficiency Imputation

Generating Concise Patches for Newly Released Programming Assignments

1 code implementation IEEE Transactions on Software Engineering 2023 Leping Li, Hui Liu, Kejun Li, Yanjie Jiang, and Rui Sun

The key to such approaches is to fix a faulty program by making it equivalent to one of its correct reference programs whose overall structure is identical to that of the faulty submission.

Deep Diversity-Enhanced Feature Representation of Hyperspectral Images

1 code implementation15 Jan 2023 Jinhui Hou, Zhiyu Zhu, Junhui Hou, Hui Liu, Huanqiang Zeng, Deyu Meng

In this paper, we study the problem of efficiently and effectively embedding the high-dimensional spatio-spectral information of hyperspectral (HS) images, guided by feature diversity.

Denoising Super-Resolution

Alignment-Enriched Tuning for Patch-Level Pre-trained Document Image Models

1 code implementation27 Nov 2022 Lei Wang, Jiabang He, Xing Xu, Ning Liu, Hui Liu

In this paper, we propose a new model architecture with alignment-enriched tuning (dubbed AETNet) upon pre-trained document image models, to adapt downstream tasks with the joint task-specific supervised and alignment-aware contrastive objective.

EGRC-Net: Embedding-induced Graph Refinement Clustering Network

1 code implementation19 Nov 2022 Zhihao Peng, Hui Liu, Yuheng Jia, Junhui Hou

To begin, we leverage both semantic and topological information by employing a vanilla auto-encoder and a graph convolution network, respectively, to learn a latent feature representation.

Clustering Graph Clustering

Contrastive Learning enhanced Author-Style Headline Generation

1 code implementation7 Nov 2022 Hui Liu, Weidong Guo, Yige Chen, Xiangyang Li

In this paper, we propose a novel Seq2Seq model called CLH3G (Contrastive Learning enhanced Historical Headlines based Headline Generation) which can use the historical headlines of the articles that the author wrote in the past to improve the headline generation of current articles.

Contrastive Learning Headline Generation

Adaptive Fuzzy Tracking Control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems

no code implementations29 Oct 2022 Bing Mao, Xiaoqun Wu, Hui Liu, Yuhua Xu, Jinhu Lü

Adaptive fuzzy control strategies are established to achieve global prescribed performance with prescribed-time convergence for strict-feedback systems with mismatched uncertainties and unknown nonlinearities.

Probabilistic Categorical Adversarial Attack & Adversarial Training

no code implementations17 Oct 2022 Han Xu, Pengfei He, Jie Ren, Yuxuan Wan, Zitao Liu, Hui Liu, Jiliang Tang

To tackle this problem, we propose Probabilistic Categorical Adversarial Attack (PCAA), which transfers the discrete optimization problem to a continuous problem that can be solved efficiently by Projected Gradient Descent.

Adversarial Attack

Nowhere to Hide: A Lightweight Unsupervised Detector against Adversarial Examples

no code implementations16 Oct 2022 Hui Liu, Bo Zhao, Kehuan Zhang, Peng Liu

In this paper, we propose an AutoEncoder-based Adversarial Examples (AEAE) detector, that can guard DNN models by detecting adversarial examples with low computation in an unsupervised manner.

Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement

1 code implementation7 Oct 2022 Hui Liu, Wenya Wang, Haoliang Li

In this paper, we propose a novel hierarchical framework for sarcasm detection by exploring both the atomic-level congruity based on multi-head cross attention mechanism and the composition-level congruity based on graph neural networks, where a post with low congruity can be identified as sarcasm.

Image Captioning Sarcasm Detection

Learning A Locally Unified 3D Point Cloud for View Synthesis

1 code implementation12 Sep 2022 Meng You, Mantang Guo, Xianqiang Lyu, Hui Liu, Junhui Hou

To tackle this challenging problem, we propose a new deep learning-based view synthesis paradigm that learns a locally unified 3D point cloud from source views.

Image Restoration

Task-Balanced Distillation for Object Detection

no code implementations5 Aug 2022 Ruining Tang, Zhenyu Liu, Yangguang Li, Yiguo Song, Hui Liu, Qide Wang, Jing Shao, Guifang Duan, Jianrong Tan

To alleviate this problem, a novel Task-decoupled Feature Distillation (TFD) is proposed by flexibly balancing the contributions of classification and regression tasks.

Classification Knowledge Distillation +4

Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation

1 code implementation21 May 2022 Yuheng Jia, Guanxing Lu, Hui Liu, Junhui Hou

In this letter, we propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix.

Clustering

Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity

no code implementations17 May 2022 Yiming Fang, Xuejun Liu, Hui Liu

The limitation can be attributed to the binding specificity of T cell receptor (TCR) to peptide-MHC complex (pMHC).

Contrastive Learning Specificity

Attention-wise masked graph contrastive learning for predicting molecular property

no code implementations2 May 2022 Hui Liu, Yibiao Huang, Xuejun Liu, Lei Deng

We developed a novel molecular graph augmentation strategy, referred to as attention-wise graph mask, to generate challenging positive sample for contrastive learning.

Contrastive Learning Graph Attention +4

Contrastive learning-based computational histopathology predict differential expression of cancer driver genes

1 code implementation25 Apr 2022 Haojie Huang, Gongming Zhou, Xuejun Liu, Lei Deng, Chen Wu, Dachuan Zhang, Hui Liu

We leveraged contrastive learning on large-scale unannotated WSIs to derive slide-level histopathological feature in latent space, and then transfer it to tumor diagnosis and prediction of differentially expressed cancer driver genes.

Contrastive Learning whole slide images

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

no code implementations18 Apr 2022 Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, Suhang Wang

Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society.

Drug Discovery Fairness

Syntax-Aware Network for Handwritten Mathematical Expression Recognition

2 code implementations CVPR 2022 Ye Yuan, Xiao Liu, Wondimu Dikubab, Hui Liu, Zhilong Ji, Zhongqin Wu, Xiang Bai

In this paper, we propose a simple and efficient method for HMER, which is the first to incorporate syntax information into an encoder-decoder network.

Content-aware Warping for View Synthesis

1 code implementation22 Jan 2022 Mantang Guo, Junhui Hou, Jing Jin, Hui Liu, Huanqiang Zeng, Jiwen Lu

To this end, we propose content-aware warping, which adaptively learns the interpolation weights for pixels of a relatively large neighborhood from their contextual information via a lightweight neural network.

Novel View Synthesis

Towards Understanding and Harnessing the Effect of Image Transformation in Adversarial Detection

no code implementations4 Jan 2022 Hui Liu, Bo Zhao, Yuefeng Peng, Weidong Li, Peng Liu

Experimental results show that the contribution of image transformations to adversarial detection is significantly different, the combination of them can significantly improve the generic detection ability against state-of-the-art adversarial attacks.

Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels

1 code implementation1 Jan 2022 Enyan Dai, Wei Jin, Hui Liu, Suhang Wang

To mitigate these issues, we propose a novel framework which adopts the noisy edges as supervision to learn a denoised and dense graph, which can down-weight or eliminate noisy edges and facilitate message passing of GNNs to alleviate the issue of limited labeled nodes.

Interpretable Low-Resource Legal Decision Making

no code implementations1 Jan 2022 Rohan Bhambhoria, Hui Liu, Samuel Dahan, Xiaodan Zhu

In this work, we utilize deep learning models in the area of trademark law to shed light on the issue of likelihood of confusion between trademarks.

Decision Making Weakly-supervised Learning

Deep Attention-guided Graph Clustering with Dual Self-supervision

1 code implementation10 Nov 2021 Zhihao Peng, Hui Liu, Yuheng Jia, Junhui Hou

Existing deep embedding clustering works only consider the deepest layer to learn a feature embedding and thus fail to well utilize the available discriminative information from cluster assignments, resulting performance limitation.

Clustering Deep Attention +1

Learning to Detect Open Carry and Concealed Object with 77GHz Radar

no code implementations31 Oct 2021 Xiangyu Gao, Hui Liu, Sumit Roy, Guanbin Xing, Ali Alansari, Youchen Luo

Detecting harmful carried objects plays a key role in intelligent surveillance systems and has widespread applications, for example, in airport security.

Adaptive Attribute and Structure Subspace Clustering Network

1 code implementation28 Sep 2021 Zhihao Peng, Hui Liu, Yuheng Jia, Junhui Hou

In this paper, we propose a novel adaptive attribute and structure subspace clustering network (AASSC-Net) to simultaneously consider the attribute and structure information in an adaptive graph fusion manner.

Attribute Clustering

Unsupervised Conversation Disentanglement through Co-Training

1 code implementation EMNLP 2021 Hui Liu, Zhan Shi, Xiaodan Zhu

For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence from the disentangled sessions predicted by the session classifier.

Conversation Disentanglement Disentanglement

Learning Dynamic Interpolation for Extremely Sparse Light Fields with Wide Baselines

1 code implementation ICCV 2021 Mantang Guo, Jing Jin, Hui Liu, Junhui Hou

In this paper, we tackle the problem of dense light field (LF) reconstruction from sparsely-sampled ones with wide baselines and propose a learnable model, namely dynamic interpolation, to replace the commonly-used geometry warping operation.

SSIM

Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild

1 code implementation ICCV 2021 Zhiyu Zhu, Hui Liu, Junhui Hou, Huanqiang Zeng, Qingfu Zhang

Specifically, on the basis of the intrinsic imaging degradation model of RGB images from HS images, we progressively spread the differences between input RGB images and re-projected RGB images from recovered HS images via effective unsupervised camera spectral response function estimation.

Image Reconstruction Spectral Reconstruction +1

Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations

1 code implementation14 Aug 2021 Lei Deng, Yibiao Huang, Xuejun Liu, Hui Liu

We evaluated our method on three independent datasets and the experimental results showed that our proposed method outperformed six existing state-of-the-art methods.

Attribute Graph Embedding

Attention-driven Graph Clustering Network

2 code implementations12 Aug 2021 Zhihao Peng, Hui Liu, Yuheng Jia, Junhui Hou

The combination of the traditional convolutional network (i. e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-encoder extracts the node attribute feature and the graph convolutional network captures the topological graph feature.

Attribute Clustering +2

Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images

1 code implementation12 Aug 2021 Zhiyu Zhu, Hui Liu, Junhui Hou, Sen Jia, Qingfu Zhang

Then, we design a lightweight neural network with a multi-stage architecture to mimic the formed amended gradient descent process, in which efficient convolution and novel spectral zero-mean normalization are proposed to effectively extract spatial-spectral features for regressing an initialization, a basic gradient, and an incremental gradient.

Spectral Reconstruction

Enhancing Descriptive Image Captioning with Natural Language Inference

1 code implementation ACL 2021 Zhan Shi, Hui Liu, Xiaodan Zhu

In this paper we propose a novel approach to encourage captioning models to produce more detailed captions using natural language inference, based on the motivation that, among different captions of an image, descriptive captions are more likely to entail less descriptive captions.

Descriptive Image Captioning +1

Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features

no code implementations19 Jul 2021 Hui Liu, Bo Zhao, Minzhi Ji, Yuefeng Peng, Jiabao Guo, Peng Liu

In this paper, we reveal that imperceptible adversarial examples are the product of recessive features misleading neural networks, and an adversarial attack is essentially a kind of method to enrich these recessive features in the image.

Adversarial Attack

AutoLoss: Automated Loss Function Search in Recommendations

no code implementations12 Jun 2021 Xiangyu Zhao, Haochen Liu, Wenqi Fan, Hui Liu, Jiliang Tang, Chong Wang

Unlike existing algorithms, the proposed controller can adaptively generate the loss probabilities for different data examples according to their varied convergence behaviors.

Recommendation Systems

Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots

1 code implementation19 May 2021 Jia-Chen Gu, Hui Liu, Zhen-Hua Ling, Quan Liu, Zhigang Chen, Xiaodan Zhu

Empirical studies on the Persona-Chat dataset show that the partner personas neglected in previous studies can improve the accuracy of response selection in the IMN- and BERT-based models.

Retrieval

Hidden Backdoors in Human-Centric Language Models

1 code implementation1 May 2021 Shaofeng Li, Hui Liu, Tian Dong, Benjamin Zi Hao Zhao, Minhui Xue, Haojin Zhu, Jialiang Lu

We are able to demonstrate the adversary's high success rate of attacks, while maintaining functionality for regular users, with triggers inconspicuous by the human administrators.

Language Modelling Machine Translation +2

Self-supervised Symmetric Nonnegative Matrix Factorization

1 code implementation2 Mar 2021 Yuheng Jia, Hui Liu, Junhui Hou, Sam Kwong, Qingfu Zhang

Inspired by ensemble clustering that aims to seek a better clustering result from a set of clustering results, we propose self-supervised SNMF (S$^3$NMF), which is capable of boosting clustering performance progressively by taking advantage of the sensitivity to initialization characteristic of SNMF, without relying on any additional information.

Clustering

Thermal properties of light mesons from holography

no code implementations22 Feb 2021 Xuanmin Cao, Songyu Qiu, Hui Liu, Danning Li

The thermal widths increase rapidly above the chiral crossover temperature $T_{cp}$, indicating the dissociations of mesons at high temperature.

High Energy Physics - Phenomenology High Energy Physics - Theory

Light Field Reconstruction via Deep Adaptive Fusion of Hybrid Lenses

1 code implementation14 Feb 2021 Jing Jin, Mantang Guo, Junhui Hou, Hui Liu, Hongkai Xiong

Besides, to promote the effectiveness of our method trained with simulated hybrid data on real hybrid data captured by a hybrid LF imaging system, we carefully design the network architecture and the training strategy.

Contact three-manifolds with exactly two simple Reeb orbits

no code implementations9 Feb 2021 Dan Cristofaro-Gardiner, Umberto Hryniewicz, Michael Hutchings, Hui Liu

It is known that every contact form on a closed three-manifold has at least two simple Reeb orbits, and a generic contact form has infinitely many.

Symplectic Geometry Dynamical Systems

Learning Light-Weight Translation Models from Deep Transformer

1 code implementation27 Dec 2020 Bei Li, Ziyang Wang, Hui Liu, Quan Du, Tong Xiao, Chunliang Zhang, Jingbo Zhu

We proposed a novel group-permutation based knowledge distillation approach to compressing the deep Transformer model into a shallow model.

Knowledge Distillation Machine Translation +2

Clustering Ensemble Meets Low-rank Tensor Approximation

1 code implementation16 Dec 2020 Yuheng Jia, Hui Liu, Junhui Hou, Qingfu Zhang

The existing clustering ensemble methods generally construct a co-association matrix, which indicates the pairwise similarity between samples, as the weighted linear combination of the connective matrices from different base clusterings, and the resulting co-association matrix is then adopted as the input of an off-the-shelf clustering algorithm, e. g., spectral clustering.

Clustering Clustering Ensemble

Maximum Entropy Subspace Clustering Network

2 code implementations6 Dec 2020 Zhihao Peng, Yuheng Jia, Hui Liu, Junhui Hou, Qingfu Zhang

Furthermore, we design a novel framework to explicitly decouple the auto-encoder module and the self-expressiveness module.

Clustering

RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition

3 code implementations13 Nov 2020 Xiangyu Gao, Guanbin Xing, Sumit Roy, Hui Liu

Millimeter-wave radars are being increasingly integrated into commercial vehicles to support new advanced driver-assistance systems by enabling robust and high-performance object detection, localization, as well as recognition - a key component of new environmental perception.

object-detection Object Detection +1

GreedyFool: Multi-Factor Imperceptibility and Its Application to Designing a Black-box Adversarial Attack

1 code implementation14 Oct 2020 Hui Liu, Bo Zhao, Minzhi Ji, Peng Liu

Adversarial examples are well-designed input samples, in which perturbations are imperceptible to the human eyes, but easily mislead the output of deep neural networks (DNNs).

Adversarial Attack

Shallow-to-Deep Training for Neural Machine Translation

1 code implementation EMNLP 2020 Bei Li, Ziyang Wang, Hui Liu, Yufan Jiang, Quan Du, Tong Xiao, Huizhen Wang, Jingbo Zhu

We find that stacking layers is helpful in improving the representation ability of NMT models and adjacent layers perform similarly.

Machine Translation NMT +2

Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning

1 code implementation EMNLP 2020 Haochen Liu, Wentao Wang, Yiqi Wang, Hui Liu, Zitao Liu, Jiliang Tang

Extensive experiments on two real-world conversation datasets show that our framework significantly reduces gender bias in dialogue models while maintaining the response quality.

Dialogue Generation

Yet Meta Learning Can Adapt Fast, It Can Also Break Easily

no code implementations2 Sep 2020 Han Xu, Ya-Xin Li, Xiaorui Liu, Hui Liu, Jiliang Tang

Thus, in this paper, we perform the initial study about adversarial attacks on meta learning under the few-shot classification problem.

Few-Shot Image Classification Meta-Learning

Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection

1 code implementation31 Aug 2020 Zhiwei Wang, Zhengzhang Chen, Jingchao Ni, Hui Liu, Haifeng Chen, Jiliang Tang

To address these challenges, in this paper, we propose OC4Seq, a multi-scale one-class recurrent neural network for detecting anomalies in discrete event sequences.

Anomaly Detection

Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization

no code implementations ACL 2020 Yue Cao, Hui Liu, Xiaojun Wan

However, it is a big challenge for the model to directly learn cross-lingual summarization as it requires learning to understand different languages and learning how to summarize at the same time.

Cross-Lingual Transfer

Memory-efficient Embedding for Recommendations

no code implementations26 Jun 2020 Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, Bo Long

Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.

AutoML Recommendation Systems

Sequence to Point Learning Based on Bidirectional Dilated Residual Network for Non Intrusive Load Monitoring

no code implementations30 May 2020 Ziyue Jia, Linfeng Yang, Zhenrong Zhang, Hui Liu, Fannie Kong

Non Intrusive Load Monitoring (NILM) or Energy Disaggregation (ED), seeks to save energy by decomposing corresponding appliances power reading from an aggregate power reading of the whole house.

blind source separation Non-Intrusive Load Monitoring

Attacking Black-box Recommendations via Copying Cross-domain User Profiles

no code implementations17 May 2020 Wenqi Fan, Tyler Derr, Xiangyu Zhao, Yao Ma, Hui Liu, Jian-Ping Wang, Jiliang Tang, Qing Li

In this work, we present our framework CopyAttack, which is a reinforcement learning based black-box attack method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items.

Data Poisoning Recommendation Systems

Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation

1 code implementation ACL 2020 Bei Li, Hui Liu, Ziyang Wang, Yufan Jiang, Tong Xiao, Jingbo Zhu, Tongran Liu, Changliang Li

In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence.

Machine Translation NMT +2

Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation

no code implementations30 Apr 2020 Yuheng Jia, Hui Liu, Junhui Hou, Sam Kwong, Qingfu Zhang

On the basis of the novel tensor low-rank norm, we formulate MVSC as a convex low-rank tensor recovery problem, which is then efficiently solved with an augmented Lagrange multiplier based method iteratively.

Clustering

RODNet: Radar Object Detection Using Cross-Modal Supervision

1 code implementation3 Mar 2020 Yizhou Wang, Zhongyu Jiang, Xiangyu Gao, Jenq-Neng Hwang, Guanbin Xing, Hui Liu

Radar is usually more robust than the camera in severe driving scenarios, e. g., weak/strong lighting and bad weather.

Autonomous Driving Object +3

Experiments with mmWave Automotive Radar Test-bed

1 code implementation29 Dec 2019 Xiangyu Gao, Guanbin Xing, Sumit Roy, Hui Liu

Millimeter-wave (mmW) radars are being increasingly integrated in commercial vehicles to support new Adaptive Driver Assisted Systems (ADAS) for its ability to provide high accuracy location, velocity, and angle estimates of objects, largely independent of environmental conditions.

Object Recognition

Learning Multi-level Dependencies for Robust Word Recognition

2 code implementations22 Nov 2019 Zhiwei Wang, Hui Liu, Jiliang Tang, Songfan Yang, Gale Yan Huang, Zitao Liu

Robust language processing systems are becoming increasingly important given the recent awareness of dangerous situations where brittle machine learning models can be easily broken with the presence of noises.

Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving

no code implementations23 Oct 2019 Rulin Shao, Hongyu He, Hui Liu, Dianbo Liu

Specifically, we design, implement and evaluate a channel-based update algorithm for the central server in a distributed system, which selects the channels with regard to the most active features in a training loop and uploads them as learned information from local datasets.

Federated Learning Privacy Preserving

Does Gender Matter? Towards Fairness in Dialogue Systems

1 code implementation COLING 2020 Haochen Liu, Jamell Dacon, Wenqi Fan, Hui Liu, Zitao Liu, Jiliang Tang

In particular, we construct a benchmark dataset and propose quantitative measures to understand fairness in dialogue models.

Fairness

Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning

no code implementations4 Oct 2019 Rulin Shao, Hui Liu, Dianbo Liu

Artificial neural network has achieved unprecedented success in a wide variety of domains such as classifying, predicting and recognizing objects.

Federated Learning Network Pruning +1

Adversarial Attacks and Defenses in Images, Graphs and Text: A Review

4 code implementations17 Sep 2019 Han Xu, Yao Ma, Haochen Liu, Debayan Deb, Hui Liu, Jiliang Tang, Anil K. Jain

In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for the three popular data types, i. e., images, graphs and text.

Adversarial Attack

DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems

no code implementations9 Sep 2019 Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang, Xiwang Yang, Xiaobing Liu, Jiliang Tang, Hui Liu

However, most RL-based advertising algorithms focus on optimizing ads' revenue while ignoring the possible negative influence of ads on user experience of recommended items (products, articles and videos).

Recommendation Systems reinforcement-learning +1

INS: An Interactive Chinese News Synthesis System

no code implementations NAACL 2019 Hui Liu, Wentao Qin, Xiaojun Wan

So it is of vital importance to automatically synthesize a batch of news articles related to the event or topic into a new synthesis article (or overview article) for reader's convenience.

Clustering-aware Graph Construction: A Joint Learning Perspective

no code implementations4 May 2019 Yuheng Jia, Hui Liu, Junhui Hou, Sam Kwong

Graph-based clustering methods have demonstrated the effectiveness in various applications.

Clustering Graph Clustering +1

Whole-Chain Recommendations

no code implementations11 Feb 2019 Xiangyu Zhao, Long Xia, Linxin Zou, Hui Liu, Dawei Yin, Jiliang Tang

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems.

Multi-agent Reinforcement Learning Recommendation Systems +1

Towards Explainable NLP: A Generative Explanation Framework for Text Classification

no code implementations ACL 2019 Hui Liu, Qingyu Yin, William Yang Wang

Building explainable systems is a critical problem in the field of Natural Language Processing (NLP), since most machine learning models provide no explanations for the predictions.

BIG-bench Machine Learning General Classification +2

Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture

no code implementations25 May 2018 Dan Nguyen, Xun Jia, David Sher, Mu-Han Lin, Zohaib Iqbal, Hui Liu, Steve Jiang

The treatment planning process for patients with head and neck (H&N) cancer is regarded as one of the most complicated due to large target volume, multiple prescription dose levels, and many radiation-sensitive critical structures near the target.

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