Search Results for author: Qian Li

Found 59 papers, 18 papers with code

Harnessing Privileged Information for Hyperbole Detection

no code implementations ALTA 2021 Rhys Biddle, Maciek Rybinski, Qian Li, Cecile Paris, Guandong Xu

The detection of hyperbole is an important stepping stone to understanding the intentions of a hyperbolic utterance.

Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition

no code implementations19 Dec 2022 Qian Li, Yuxiao Hu, Ye Liu, Dongxiao Zhang, Xin Jin, Yuntian Chen

Classical adversarial attacks for Face Recognition (FR) models typically generate discrete examples for target identity with a single state image.

Adversarial Attack Data Augmentation +1

Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems

no code implementations15 Nov 2022 Qian Li, JianXin Li, Lihong Wang, Cheng Ji, Yiming Hei, Jiawei Sheng, Qingyun Sun, Shan Xue, Pengtao Xie

To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED, leveraging a semantic channel and a topological channel to enrich information interaction from short texts.

Event Detection Semantic Similarity +1

Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames

1 code implementation9 Nov 2022 Qi Li, Ziyi Shen, Qian Li, Dean C Barratt, Thomas Dowrick, Matthew J Clarkson, Tom Vercauteren, Yipeng Hu

Little benefit was observed by adding frames more than one second away from the predicted transformation, with or without LSTM-based RNNs.

Multi-Task Learning

Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning

1 code implementation8 Nov 2022 Qian Li, Shafiq Joty, Daling Wang, Shi Feng, Yifei Zhang

Sparsity of formal knowledge and roughness of non-ontological construction make sparsity problem particularly prominent in Open Knowledge Graphs (OpenKGs).

Contrastive Learning Knowledge Graphs

FedMCSA: Personalized Federated Learning via Model Components Self-Attention

no code implementations23 Aug 2022 Qi Guo, Yong Qi, Saiyu Qi, Di wu, Qian Li

Federated learning (FL) facilitates multiple clients to jointly train a machine learning model without sharing their private data.

Personalized Federated Learning

Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing

1 code implementation17 Aug 2022 Qingyun Sun, JianXin Li, Haonan Yuan, Xingcheng Fu, Hao Peng, Cheng Ji, Qian Li, Philip S. Yu

Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs.

Graph Learning Graph structure learning +1

Learning Generalizable Light Field Networks from Few Images

no code implementations24 Jul 2022 Qian Li, Franck Multon, Adnane Boukhayma

We explore a new strategy for few-shot novel view synthesis based on a neural light field representation.

Novel View Synthesis

Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning

no code implementations1 Jul 2022 Haoran Yang, Hongxu Chen, Sixiao Zhang, Xiangguo Sun, Qian Li, Guandong Xu

In this paper, we propose a novel method to utilize \textbf{C}ounterfactual mechanism to generate artificial hard negative samples for \textbf{G}raph \textbf{C}ontrastive learning, namely \textbf{CGC}, which has a different perspective compared to those sampling-based strategies.

Contrastive Learning Graph Learning +1

Forestry digital twin with machine learning in Landsat 7 data

no code implementations2 Apr 2022 XueTao Jiang, Meiyu Jiang, YuChun Gou, Qian Li, Qingguo Zhou

In this paper, we propose an LSTM-based digital twin approach for forest modeling, using Landsat 7 remote sensing image within 20 years.

BIG-bench Machine Learning Time Series

New Distinguishers for Negation-Limited Weak Pseudorandom Functions

no code implementations23 Mar 2022 Zhihuai Chen, Siyao Guo, Qian Li, Chengyu Lin, Xiaoming Sun

We show how to distinguish circuits with $\log k$ negations (a. k. a $k$-monotone functions) from uniformly random functions in $\exp\left(\tilde{O}\left(n^{1/3}k^{2/3}\right)\right)$ time using random samples.

Universal Segmentation of 33 Anatomies

no code implementations4 Mar 2022 Pengbo Liu, Yang Deng, Ce Wang, Yuan Hui, Qian Li, Jun Li, Shiwei Luo, Mengke Sun, Quan Quan, Shuxin Yang, You Hao, Honghu Xiao, Chunpeng Zhao, Xinbao Wu, S. Kevin Zhou

Firstly, while it is ideal to learn such a model from a large-scale, fully-annotated dataset, it is practically hard to curate such a dataset.

Image Segmentation Medical Image Segmentation +1

Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation

no code implementations5 Feb 2022 Xiangmeng Wang, Qian Li, Dianer Yu, Peng Cui, Zhichao Wang, Guandong Xu

Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users' true intent and thus deteriorate the recommendation effectiveness.


DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction

no code implementations21 Nov 2021 Ce Wang, Kun Shang, Haimiao Zhang, Qian Li, Yuan Hui, S. Kevin Zhou

While Computed Tomography (CT) reconstruction from X-ray sinograms is necessary for clinical diagnosis, iodine radiation in the imaging process induces irreversible injury, thereby driving researchers to study sparse-view CT reconstruction, that is, recovering a high-quality CT image from a sparse set of sinogram views.

Computed Tomography (CT)

AFEC: Active Forgetting of Negative Transfer in Continual Learning

1 code implementation NeurIPS 2021 Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong

Without accessing to the old training samples, knowledge transfer from the old tasks to each new task is difficult to determine, which might be either positive or negative.

Continual Learning Transfer Learning

How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View

1 code implementation24 Sep 2021 Ren Li, Yanan Cao, Qiannan Zhu, Guanqun Bi, Fang Fang, Yi Liu, Qian Li

However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate.

Knowledge Graph Completion Knowledge Graph Embedding +1

Event Extraction by Associating Event Types and Argument Roles

no code implementations23 Aug 2021 Qian Li, Shu Guo, Jia Wu, JianXin Li, Jiawei Sheng, Lihong Wang, Xiaohan Dong, Hao Peng

It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles.

Association Event Extraction +2

AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer

3 code implementations ICCV 2021 Songhua Liu, Tianwei Lin, Dongliang He, Fu Li, Meiling Wang, Xin Li, Zhengxing Sun, Qian Li, Errui Ding

Finally, the content feature is normalized so that they demonstrate the same local feature statistics as the calculated per-point weighted style feature statistics.

Style Transfer Video Style Transfer

A Survey on Deep Learning Event Extraction: Approaches and Applications

no code implementations5 Jul 2021 Qian Li, JianXin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu

Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.

Event Extraction

Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations

1 code implementation23 Jun 2021 Qian Li, Hao Peng, JianXin Li, Jia Wu, Yuanxing Ning, Lihong Wang, Philip S. Yu, Zheng Wang

Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.

Event Extraction Incremental Learning +2

Stochastic Intervention for Causal Inference via Reinforcement Learning

no code implementations28 May 2021 Tri Dung Duong, Qian Li, Guandong Xu

In our study, we advance the causal inference research by proposing a new effective framework to estimate the treatment effect on stochastic intervention.

Causal Inference Decision Making +2

Stochastic Intervention for Causal Effect Estimation

no code implementations27 May 2021 Tri Dung Duong, Qian Li, Guandong Xu

Central to these applications is the treatment effect estimation of intervention strategies.

Causal Inference Decision Making

Be Causal: De-biasing Social Network Confounding in Recommendation

no code implementations17 May 2021 Qian Li, Xiangmeng Wang, Guandong Xu

A common practice to address MNAR is to treat missing entries from the so-called "exposure" perspective, i. e., modeling how an item is exposed (provided) to a user.

Causal Inference Recommendation Systems +2

High Accuracy and Low Complexity Frequency Offset Estimation Based on All-Phase FFT for M-QAM Coherent Optical Systems

no code implementations15 May 2021 Qian Li

A low complexity frequency offset estimation algorithm based on all-phase FFT for M-QAM is proposed.

Prototype-based Counterfactual Explanation for Causal Classification

1 code implementation3 May 2021 Tri Dung Duong, Qian Li, Guandong Xu

Counterfactual explanation is one branch of interpretable machine learning that produces a perturbation sample to change the model's original decision.

Classification Counterfactual Explanation +2

Few-shot Continual Learning: a Brain-inspired Approach

no code implementations19 Apr 2021 Liyuan Wang, Qian Li, Yi Zhong, Jun Zhu

Our solution is based on the observation that continual learning of a task sequence inevitably interferes few-shot generalization, which makes it highly nontrivial to extend few-shot learning strategies to continual learning scenarios.

Continual Learning Few-Shot Learning

A General Framework for Learning Prosodic-Enhanced Representation of Rap Lyrics

no code implementations23 Mar 2021 Hongru Liang, Haozheng Wang, Qian Li, Jun Wang, Guandong Xu, Jiawei Chen, Jin-Mao Wei, Zhenglu Yang

Learning and analyzing rap lyrics is a significant basis for many web applications, such as music recommendation, automatic music categorization, and music information retrieval, due to the abundant source of digital music in the World Wide Web.

Information Retrieval Music Information Retrieval +3

Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation

no code implementations9 Mar 2021 Ce Wang, Haimiao Zhang, Qian Li, Kun Shang, Yuanyuan Lyu, Bin Dong, S. Kevin Zhou

More importantly, we show that using such a sinogram extrapolation module significantly improves the generalization capability of the model on unseen datasets (e. g., COVID-19 and LIDC datasets) when compared to existing approaches.

Computed Tomography (CT)

Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search

no code implementations28 Sep 2020 Qian Li, Nan Guo, Xiaochun Ye, Dongrui Fan, Zhimin Tang

Ours is suitable for large-scale datasets, and experimental results show that our method is 82% faster than the violent retrieval for the single-frame detection.

Face Recognition Retrieval

Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection

no code implementations14 Sep 2020 Jun Yin, Qian Li, Shaowu Liu, Zhiang Wu, Guandong Xu

Our study investigates the spammer detection problem in the context of multi-relation social networks, and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy.

Top-Related Meta-Learning Method for Few-Shot Object Detection

1 code implementation14 Jul 2020 Qian Li, Nan Guo, Xiaochun Ye, Duo Wang, Dongrui Fan, Zhimin Tang

Therefore, based on semantic features, we propose a Top-C classification loss (i. e., TCL-C) for classification task and a category-based grouping mechanism for category-based meta-features obtained by the meta-model.

Few-Shot Object Detection Meta-Learning +1

Causality Learning: A New Perspective for Interpretable Machine Learning

no code implementations27 Jun 2020 Guandong Xu, Tri Dung Duong, Qian Li, Shaowu Liu, Xianzhi Wang

Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc.

Association BIG-bench Machine Learning +3

Stochastic Batch Augmentation with An Effective Distilled Dynamic Soft Label Regularizer

no code implementations27 Jun 2020 Qian Li, Qingyuan Hu, Yong Qi, Saiyu Qi, Jie Ma, Jian Zhang

SBA stochastically decides whether to augment at iterations controlled by the batch scheduler and in which a ''distilled'' dynamic soft label regularization is introduced by incorporating the similarity in the vicinity distribution respect to raw samples.

Data Augmentation

Weakly Supervised Context Encoder using DICOM metadata in Ultrasound Imaging

no code implementations20 Mar 2020 Szu-Yeu Hu, Shuhang Wang, Wei-Hung Weng, JingChao Wang, XiaoHong Wang, Arinc Ozturk, Qian Li, Viksit Kumar, Anthony E. Samir

Modern deep learning algorithms geared towards clinical adaption rely on a significant amount of high fidelity labeled data.

Neighborhood Information-based Probabilistic Algorithm for Network Disintegration

no code implementations8 Mar 2020 Qian Li, San-Yang Liu, Xin-She Yang

Network structure and integrity can be controlled by a set of key nodes, and to find the optimal combination of nodes in a network to ensure network structure and integrity can be an NP-complete problem.

Protein Folding

Influence of Initialization on the Performance of Metaheuristic Optimizers

no code implementations8 Mar 2020 Qian Li, San-Yang Liu, Xin-She Yang

Differential evolution depends more heavily on the number of iterations, a relatively small population with more iterations can lead to better results.

Metaheuristic Optimization

Triple Memory Networks: a Brain-Inspired Method for Continual Learning

no code implementations6 Mar 2020 Liyuan Wang, Bo Lei, Qian Li, Hang Su, Jun Zhu, Yi Zhong

Continual acquisition of novel experience without interfering previously learned knowledge, i. e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting.

class-incremental learning Hippocampus +1

Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension

no code implementations WS 2019 Qian Li, Hui Su, Cheng Niu, Daling Wang, Zekang Li, Shi Feng, Yifei Zhang

Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.

Reading Comprehension reinforcement-learning +1

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation

no code implementations27 Aug 2019 Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, Xiaowei Ding

The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks.

Image Segmentation Medical Image Segmentation +1

Are You for Real? Detecting Identity Fraud via Dialogue Interactions

1 code implementation IJCNLP 2019 Weikang Wang, Jiajun Zhang, Qian Li, Cheng-qing Zong, Zhifei Li

In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consists of two modules.

Dialogue Management Fraud Detection +1

Incremental Transformer with Deliberation Decoder for Document Grounded Conversations

2 code implementations ACL 2019 Zekang Li, Cheng Niu, Fandong Meng, Yang Feng, Qian Li, Jie zhou

Document Grounded Conversations is a task to generate dialogue responses when chatting about the content of a given document.

INFaaS: A Model-less and Managed Inference Serving System

1 code implementation30 May 2019 Francisco Romero, Qian Li, Neeraja J. Yadwadkar, Christos Kozyrakis

This paper introduces INFaaS, a managed and model-less system for distributed inference serving, where developers simply specify the performance and accuracy requirements for their applications without needing to specify a specific model-variant for each query.

Model Selection

A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task

no code implementations COLING 2018 Qian Li, Ziwei Li, Jin-Mao Wei, Yanhui Gu, Adam Jatowt, Zhenglu Yang

Enabling a mechanism to understand a temporal story and predict its ending is an interesting issue that has attracted considerable attention, as in case of the ROC Story Cloze Task (SCT).

Common Sense Reasoning Feature Engineering +1

Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising

no code implementations23 Nov 2016 Jia Zhang, Zheng Wang, Qian Li, Jialin Zhang, Yanyan Lan, Qiang Li, Xiaoming Sun

In the guaranteed delivery scenario, ad exposures (which are also called impressions in some works) to users are guaranteed by contracts signed in advance between advertisers and publishers.

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