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.
no code implementations • Findings (EMNLP) 2021 • Haoyu Wang, Fenglong Ma, Yaqing Wang, Jing Gao
We propose to mine outline knowledge of concepts related to given sentences from Wikipedia via BM25 model.
no code implementations • 6 May 2024 • Han Liu, Siyang Zhao, Xiaotong Zhang, Feng Zhang, Wei Wang, Fenglong Ma, Hongyang Chen, Hong Yu, Xianchao Zhang
Few-shot and zero-shot text classification aim to recognize samples from novel classes with limited labeled samples or no labeled samples at all.
1 code implementation • 19 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.
1 code implementation • 19 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.
no code implementations • 24 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.
no code implementations • 16 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.
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.
no code implementations • 2 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.
no code implementations • 29 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.
1 code implementation • 20 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.
no code implementations • 3 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.
no code implementations • 11 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.
1 code implementation • 2 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.
no code implementations • 29 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.
1 code implementation • 11 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.
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.
no code implementations • 4 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).
1 code implementation • 29 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?
1 code implementation • 6 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.
no code implementations • 10 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.
1 code implementation • 3 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).
no code implementations • 26 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
no code implementations • 5 Mar 2023 • Jiaqi Wang, Shenglai Zeng, Zewei Long, Yaqing Wang, Houping Xiao, Fenglong Ma
This is a new yet practical scenario in federated learning, i. e., labels-at-server semi-supervised federated learning (SemiFL).
no code implementations • 21 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.
no code implementations • 5 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.
no code implementations • 27 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.
no code implementations • 30 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.
1 code implementation • 9 May 2022 • Xiaokun Zhang, Bo Xu, Liang Yang, Chenliang Li, Fenglong Ma, Haifeng Liu, Hongfei Lin
Finally, we predict user actions based on item features and users' price and interest preferences.
no code implementations • 11 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.
no code implementations • 12 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.
no code implementations • 9 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.
no code implementations • 22 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.
no code implementations • ACL 2021 • Xingyi Yang, Muchao Ye, Quanzeng You, Fenglong Ma
Medical report generation is one of the most challenging tasks in medical image analysis.
no code implementations • 7 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.
1 code implementation • 5 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.
no code implementations • 17 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.
no code implementations • 22 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.
no code implementations • 6 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.
1 code implementation • COLING 2022 • Junyu Luo, Zifei Zheng, Hanzhong Ye, Muchao Ye, Yaqing Wang, Quanzeng You, Cao Xiao, Fenglong Ma
To fairly evaluate the performance, we also propose three specific evaluation metrics.
no code implementations • 22 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.
no code implementations • 16 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.
1 code implementation • 28 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.
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.
Ranked #5 on Nested Mention Recognition on ACE 2005
Multi-Grained Named Entity Recognition named-entity-recognition +5
no code implementations • 27 Sep 2018 • Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip S. Yu
In this paper, we focus on a new Named Entity Recognition (NER) task, i. e., the Multi-grained NER task.
1 code implementation • Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018 • Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, Jing Gao
One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events.
no code implementations • 6 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.
no code implementations • 19 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.