Search Results for author: Fenglong Ma

Found 51 papers, 16 papers with code

RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips

no code implementations COLING 2022 Bo Xu, Hongtong Zhang, Jian Wang, Xiaokun Zhang, Dezhi Hao, Linlin Zong, Hongfei Lin, Fenglong Ma

We collected and annotated a wide range of meta-data with respect to medical dialogue including doctor profiles, hospital departments, diseases and symptoms for fine-grained analysis on language usage pattern and clinical diagnosis.

Response Generation

Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality

no code implementations16 Jun 2024 Liwei Che, Jiaqi Wang, Xinyue Liu, Fenglong Ma

To tackle the problems, we propose a novel multi-modal federated learning method, Federated Multi-modal contrastiVe training with Pre-trained completion (FedMVP), which integrates the large-scale pre-trained models to enhance the federated training.

Federated Learning Image-text Classification +3

FineRec:Exploring Fine-grained Sequential Recommendation

1 code implementation19 Apr 2024 Xiaokun Zhang, Bo Xu, Youlin Wu, Yuan Zhong, Hongfei Lin, Fenglong Ma

Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors.

Attribute Diversity +4

Disentangling ID and Modality Effects for Session-based Recommendation

1 code implementation19 Apr 2024 Xiaokun Zhang, Bo Xu, Zhaochun Ren, Xiaochen Wang, Hongfei Lin, Fenglong Ma

At the item level, we introduce a co-occurrence representation schema to explicitly incorporate cooccurrence patterns into ID representations.

counterfactual Counterfactual Inference +2

CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning

no code implementations24 Feb 2024 Junyu Luo, Xiaochen Wang, Jiaqi Wang, Aofei Chang, Yaqing Wang, Fenglong Ma

Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing.


VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models

no code implementations16 Feb 2024 Ziyi Yin, Muchao Ye, Tianrong Zhang, Jiaqi Wang, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma

Correspondingly, we propose a novel VQAttack model, which can iteratively generate both image and text perturbations with the designed modules: the large language model (LLM)-enhanced image attack and the cross-modal joint attack module.

Adversarial Robustness Language Modelling +3

HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text

1 code implementation NeurIPS 2023 Han Liu, Zhi Xu, Xiaotong Zhang, Feng Zhang, Fenglong Ma, Hongyang Chen, Hong Yu, Xianchao Zhang

Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted label is accessible.

Adversarial Attack Hard-label Attack +5

Recent Advances in Predictive Modeling with Electronic Health Records

no code implementations2 Feb 2024 Jiaqi Wang, Junyu Luo, Muchao Ye, Xiaochen Wang, Yuan Zhong, Aofei Chang, Guanjie Huang, Ziyi Yin, Cao Xiao, Jimeng Sun, Fenglong Ma

This survey systematically reviews recent advances in deep learning-based predictive models using EHR data.

Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation

no code implementations29 Jan 2024 Jiaqi Wang, Yuzhong Chen, Yuhang Wu, Mahashweta Das, Hao Yang, Fenglong Ma

Subsequently, we design a precise personalized model distribution strategy to allow clients to obtain the most suitable model from the server side.

Clustering Personalized Federated Learning

Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions

1 code implementation20 Jan 2024 Suhan Cui, Jiaqi Wang, Yuan Zhong, Han Liu, Ting Wang, Fenglong Ma

The widespread adoption of Electronic Health Record (EHR) systems in healthcare institutes has generated vast amounts of medical data, offering significant opportunities for improving healthcare services through deep learning techniques.

Neural Architecture Search

Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction

no code implementations3 Jan 2024 Wei Qian, Chenxu Zhao, Yangyi Li, Fenglong Ma, Chao Zhang, Mengdi Huai

To tackle the aforementioned challenges, in this paper, we design a novel uncertainty modeling framework for self-explaining networks, which not only demonstrates strong distribution-free uncertainty modeling performance for the generated explanations in the interpretation layer but also excels in producing efficient and effective prediction sets for the final predictions based on the informative high-level basis explanations.

Conformal Prediction Uncertainty Quantification

Mitigating Pooling Bias in E-commerce Search via False Negative Estimation

no code implementations11 Nov 2023 Xiaochen Wang, Xiao Xiao, Ruhan Zhang, Xuan Zhang, Taesik Na, Tejaswi Tenneti, Haixun Wang, Fenglong Ma

Efficient and accurate product relevance assessment is critical for user experiences and business success.

Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation

1 code implementation2 Nov 2023 Xiaokun Zhang, Bo Xu, Fenglong Ma, Chenliang Li, Yuan Lin, Hongfei Lin

Secondly, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling.

Multi-Task Learning Session-Based Recommendations

Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors

no code implementations29 Oct 2023 Han Liu, Xingshuo Huang, Xiaotong Zhang, Qimai Li, Fenglong Ma, Wei Wang, Hongyang Chen, Hong Yu, Xianchao Zhang

Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction.

Adversarial Attack

Hierarchical Pretraining on Multimodal Electronic Health Records

1 code implementation11 Oct 2023 Xiaochen Wang, Junyu Luo, Jiaqi Wang, Ziyi Yin, Suhan Cui, Yuan Zhong, Yaqing Wang, Fenglong Ma

Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks.

VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models

1 code implementation NeurIPS 2023 Ziyi Yin, Muchao Ye, Tianrong Zhang, Tianyu Du, Jinguo Zhu, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma

In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks.

Adversarial Robustness

MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation

no code implementations4 Oct 2023 Yuan Zhong, Suhan Cui, Jiaqi Wang, Xiaochen Wang, Ziyi Yin, Yaqing Wang, Houping Xiao, Mengdi Huai, Ting Wang, Fenglong Ma

Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in the future using their historical Electronic Health Records (EHR).

Data Augmentation

Beyond Co-occurrence: Multi-modal Session-based Recommendation

1 code implementation29 Sep 2023 Xiaokun Zhang, Bo Xu, Fenglong Ma, Chenliang Li, Liang Yang, Hongfei Lin

(2) How to fuse these heterogeneous descriptive information to comprehensively infer user interests?

Contrastive Learning Descriptive +2

Zero-Resource Hallucination Prevention for Large Language Models

1 code implementation6 Sep 2023 Junyu Luo, Cao Xiao, Fenglong Ma

Existing techniques for hallucination detection in language assistants rely on intricate fuzzy, specific free-language-based chain of thought (CoT) techniques or parameter-based methods that suffer from interpretability issues.


Hate Speech Detection via Dual Contrastive Learning

no code implementations10 Jul 2023 Junyu Lu, Hongfei Lin, Xiaokun Zhang, Zhaoqing Li, Tongyue Zhang, Linlin Zong, Fenglong Ma, Bo Xu

Our framework jointly optimizes the self-supervised and the supervised contrastive learning loss for capturing span-level information beyond the token-level emotional semantics used in existing models, particularly detecting speech containing abusive and insulting words.

Contrastive Learning Hate Speech Detection

On the Security Risks of Knowledge Graph Reasoning

1 code implementation3 May 2023 Zhaohan Xi, Tianyu Du, Changjiang Li, Ren Pang, Shouling Ji, Xiapu Luo, Xusheng Xiao, Fenglong Ma, Ting Wang

Knowledge graph reasoning (KGR) -- answering complex logical queries over large knowledge graphs -- represents an important artificial intelligence task, entailing a range of applications (e. g., cyber threat hunting).

Knowledge Graphs

Boosting Few-Shot Text Classification via Distribution Estimation

no code implementations26 Mar 2023 Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Fenglong Ma, Xiao-Ming Wu, Hongyang Chen, Hong Yu, Xianchao Zhang

Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain.

Few-Shot Image Classification Few-Shot Text Classification +1

AutoML in The Wild: Obstacles, Workarounds, and Expectations

no code implementations21 Feb 2023 Yuan Sun, Qiurong Song, Xinning Gui, Fenglong Ma, Ting Wang

Automated machine learning (AutoML) is envisioned to make ML techniques accessible to ordinary users.


Forecasting User Interests Through Topic Tag Predictions in Online Health Communities

no code implementations5 Nov 2022 Amogh Subbakrishna Adishesha, Lily Jakielaszek, Fariha Azhar, Peixuan Zhang, Vasant Honavar, Fenglong Ma, Chandra Belani, Prasenjit Mitra, Sharon Xiaolei Huang

Specifically, we pose the problem of predicting topic tags or keywords that describe the future information needs of users based on their profiles, traces of their online interactions within the community (past posts, replies) and the profiles and traces of online interactions of other users with similar profiles and similar traces of past interaction with the target users.

Recommendation Systems Text2text Generation

Reasoning over Multi-view Knowledge Graphs

no code implementations27 Sep 2022 Zhaohan Xi, Ren Pang, Changjiang Li, Tianyu Du, Shouling Ji, Fenglong Ma, Ting Wang

(ii) It supports complex logical queries with varying relation and view constraints (e. g., with complex topology and/or from multiple views); (iii) It scales up to KGs of large sizes (e. g., millions of facts) and fine-granular views (e. g., dozens of views); (iv) It generalizes to query structures and KG views that are unobserved during training.

Knowledge Graphs Representation Learning

Predicting Ulnar Collateral Ligament Injury in Rookie Major League Baseball Pitchers

no code implementations30 Jun 2022 Sean A. Rendar, Fenglong Ma

In the growing world of machine learning and data analytics, scholars are finding new and innovative ways to solve real-world problems.

BIG-bench Machine Learning

MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare

no code implementations11 Dec 2021 Muchao Ye, Junyu Luo, Guanjie Zheng, Cao Xiao, Ting Wang, Fenglong Ma

Deep neural networks (DNNs) have been broadly adopted in health risk prediction to provide healthcare diagnoses and treatments.

Adversarial Attack Position +1

FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning

no code implementations12 Sep 2021 Liwei Che, Zewei Long, Jiaqi Wang, Yaqing Wang, Houping Xiao, Fenglong Ma

In particular, we propose to use three networks and a dynamic quality control mechanism to generate high-quality pseudo labels for unlabeled data, which are added to the training set.

Federated Learning

FedCon: A Contrastive Framework for Federated Semi-Supervised Learning

no code implementations9 Sep 2021 Zewei Long, Jiaqi Wang, Yaqing Wang, Houping Xiao, Fenglong Ma

Most existing FedSSL methods focus on the classical scenario, i. e, the labeled and unlabeled data are stored at the client side.

Multimodal Emergent Fake News Detection via Meta Neural Process Networks

no code implementations22 Jun 2021 Yaqing Wang, Fenglong Ma, Haoyu Wang, Kishlay Jha, Jing Gao

The experimental results show our proposed MetaFEND model can detect fake news on never-seen events effectively and outperform the state-of-the-art methods.

Fake News Detection Hard Attention +1

ConCAD: Contrastive Learning-based Cross Attention for Sleep Apnea Detection

no code implementations7 May 2021 Guanjie Huang, Fenglong Ma

With recent advancements in deep learning methods, automatically learning deep features from the original data is becoming an effective and widespread approach.

Contrastive Learning Sleep apnea detection

SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations

1 code implementation5 May 2021 Chaoqi Yang, Cao Xiao, Fenglong Ma, Lucas Glass, Jimeng Sun

On a benchmark dataset, our SafeDrug is relatively shown to reduce DDI by 19. 43% and improves 2. 88% on Jaccard similarity between recommended and actually prescribed drug combinations over previous approaches.

Fairness-aware Outlier Ensemble

no code implementations17 Mar 2021 Haoyu Liu, Fenglong Ma, Shibo He, Jiming Chen, Jing Gao

Meanwhile, we propose a post-processing framework to tune the original ensemble results through a stacking process so that we can achieve a trade off between fairness and detection performance.

Fairness Fraud Detection +1

i-Algebra: Towards Interactive Interpretability of Deep Neural Networks

no code implementations22 Jan 2021 Xinyang Zhang, Ren Pang, Shouling Ji, Fenglong Ma, Ting Wang

Providing explanations for deep neural networks (DNNs) is essential for their use in domains wherein the interpretability of decisions is a critical prerequisite.

FedSiam: Towards Adaptive Federated Semi-Supervised Learning

no code implementations6 Dec 2020 Zewei Long, Liwei Che, Yaqing Wang, Muchao Ye, Junyu Luo, Jinze Wu, Houping Xiao, Fenglong Ma

In this paper, we focus on designing a general framework FedSiam to tackle different scenarios of federated semi-supervised learning, including four settings in the labels-at-client scenario and two setting in the labels-at-server scenario.

Federated Learning

UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data

no code implementations22 Oct 2020 Chacha Chen, Junjie Liang, Fenglong Ma, Lucas M. Glass, Jimeng Sun, Cao Xiao

However, existing uncertainty estimation approaches often failed in handling high-dimensional data, which are present in multi-sourced data.

Clustering Variational Inference

Efficient Knowledge Graph Validation via Cross-Graph Representation Learning

no code implementations16 Aug 2020 Yaqing Wang, Fenglong Ma, Jing Gao

To tackle this challenging task, we propose a cross-graph representation learning framework, i. e., CrossVal, which can leverage an external KG to validate the facts in the target KG efficiently.

Graph Representation Learning Knowledge Graphs

Weak Supervision for Fake News Detection via Reinforcement Learning

1 code implementation28 Dec 2019 Yaqing Wang, Weifeng Yang, Fenglong Ma, Jin Xu, Bin Zhong, Qiang Deng, Jing Gao

In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i. e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection.

Fake News Detection reinforcement-learning +1

Multi-Grained Named Entity Recognition

1 code implementation ACL 2019 Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip Yu

This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.

Multi-Grained Named Entity Recognition named-entity-recognition +5

Long-Term Memory Networks for Question Answering

no code implementations6 Jul 2017 Fenglong Ma, Radha Chitta, Saurabh Kataria, Jing Zhou, Palghat Ramesh, Tong Sun, Jing Gao

Question answering is an important and difficult task in the natural language processing domain, because many basic natural language processing tasks can be cast into a question answering task.

Question Answering

Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks

no code implementations19 Jun 2017 Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, Jing Gao

Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results.

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