8 code implementations • 26 Feb 2019 • Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B. Schardl, Charles E. Leiserson
Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
Ranked #4 on Dynamic Link Prediction on DBLP Temporal
3 code implementations • ICLR 2018 • Jie Chen, Tengfei Ma, Cao Xiao
The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning.
Ranked #3 on Node Classification on Citeseer Full-supervised
2 code implementations • 30 Nov 2018 • Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, Tao B. Schardl
Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150, 000 people since 2006, upwards of 700, 000 people per year are "exported" in a human trafficking industry enslaving an estimated 40 million people.
1 code implementation • NeurIPS 2018 • Tengfei Ma, Jie Chen, Cao Xiao
We focus on the matrix representation of graphs and formulate penalty terms that regularize the output distribution of the decoder to encourage the satisfaction of validity constraints.
1 code implementation • 6 Sep 2018 • Junyuan Shang, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions.
1 code implementation • 2 Jun 2019 • Junyuan Shang, Tengfei Ma, Cao Xiao, Jimeng Sun
G-BERT is the first to bring the language model pre-training schema into the healthcare domain and it achieved state-of-the-art performance on the medication recommendation task.
1 code implementation • 27 Feb 2023 • Arpandeep Khatua, Vikram Sharma Mailthody, Bhagyashree Taleka, Tengfei Ma, Xiang Song, Wen-mei Hwu
Most existing public datasets for GNNs are relatively small, which limits the ability of GNNs to generalize to unseen data.
2 code implementations • 1 Nov 2018 • Tengfei Ma, Patrick Ferber, Siyu Huo, Jie Chen, Michael Katz
Automated planning is one of the foundational areas of AI.
1 code implementation • 15 May 2019 • Patrick Ferber, Tengfei Ma, Siyu Huo, Jie Chen, Michael Katz
Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods.
Ranked #2 on Graph Classification on IPC-lifted
1 code implementation • 8 Jul 2020 • Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji
In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs.
1 code implementation • 24 Dec 2019 • Tengfei Ma, Jie Chen
Both the coarsening matrix and the transport cost matrix are parameterized, so that an optimal coarsening strategy can be learned and tailored for a given set of graphs.
1 code implementation • 27 May 2019 • Shenda Hong, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun
Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities.
1 code implementation • 28 Apr 2018 • Tengfei Ma, Cao Xiao, Jiayu Zhou, Fei Wang
In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features.
1 code implementation • EMNLP 2016 • Long Duong, Hiroshi Kanayama, Tengfei Ma, Steven Bird, Trevor Cohn
Crosslingual word embeddings represent lexical items from different languages in the same vector space, enabling transfer of NLP tools.
Bilingual Lexicon Induction Cross-Lingual Document Classification +4
1 code implementation • EMNLP 2020 • Hanlu Wu, Tengfei Ma, Lingfei Wu, Tariro Manyumwa, Shouling Ji
Experiments on Newsroom and CNN/Daily Mail demonstrate that our new evaluation method outperforms other metrics even without reference summaries.
1 code implementation • EMNLP 2021 • Manling Li, Tengfei Ma, Mo Yu, Lingfei Wu, Tian Gao, Heng Ji, Kathleen McKeown
Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged.
1 code implementation • 20 Feb 2021 • Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen
Link prediction is an important learning task for graph-structured data.
2 code implementations • 10 Jul 2021 • Mehdi Ali, Max Berrendorf, Mikhail Galkin, Veronika Thost, Tengfei Ma, Volker Tresp, Jens Lehmann
In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks.
1 code implementation • 28 Jan 2022 • Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Yusu Wang, Chao Chen
Topological features based on persistent homology capture high-order structural information so as to augment graph neural network methods.
1 code implementation • 6 Oct 2021 • Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen
In this paper, we consider rules as cycles and show that the space of cycles has a unique structure based on the mathematics of algebraic topology.
Graph Representation Learning Inductive Relation Prediction +1
1 code implementation • NAACL 2022 • Weimin Lyu, Songzhu Zheng, Tengfei Ma, Chao Chen
Trojan attacks raise serious security concerns.
1 code implementation • 24 Oct 2023 • Tong Ye, Lingfei Wu, Tengfei Ma, Xuhong Zhang, Yangkai Du, Peiyu Liu, Shouling Ji, Wenhai Wang
Automatically generating function summaries for binaries is an extremely valuable but challenging task, since it involves translating the execution behavior and semantics of the low-level language (assembly code) into human-readable natural language.
1 code implementation • 6 Sep 2018 • Shenda Hong, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li, Jimeng Sun
In many situations, we need to build and deploy separate models in related environments with different data qualities.
1 code implementation • Findings (EMNLP) 2021 • Yangkai Du, Tengfei Ma, Lingfei Wu, Fangli Xu, Xuhong Zhang, Bo Long, Shouling Ji
Unlike vision tasks, the data augmentation method for contrastive learning has not been investigated sufficiently in language tasks.
1 code implementation • 2 Jun 2023 • Tengfei Ma, Trong Nghia Hoang, Jie Chen
Second, we need to learn a consensus graph that captures the high-order interactions between local feature spaces and how to combine them to achieve a better prediction.
1 code implementation • 18 May 2023 • Tong Ye, Lingfei Wu, Tengfei Ma, Xuhong Zhang, Yangkai Du, Peiyu Liu, Shouling Ji, Wenhai Wang
In this paper, we propose a fine-grained Token-level retrieval-augmented mechanism (Tram) on the decoder side rather than the encoder side to enhance the performance of neural models and produce more low-frequency tokens in generating summaries.
1 code implementation • 24 Nov 2023 • Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen, Yusu Wang
To efficiently encode the space of all cycles, we start with a cycle basis (i. e., a minimal set of cycles generating the cycle space) which we compute via the kernel of the 1-dimensional Hodge Laplacian of the input graph.
no code implementations • 21 Dec 2016 • Tengfei Ma, Tetsuya Nasukawa
In this paper, we try to address two challenges of applying topic models to lexicon extraction in non-parallel data: 1) hard to model the word relationship and 2) noisy seed dictionary.
no code implementations • 11 Sep 2018 • Tengfei Ma, Chiamin Wu, Cao Xiao, Jimeng Sun
It refers to the directional relation between text fragments such that the "premise" can infer "hypothesis".
no code implementations • 7 Dec 2018 • Zhongshu Gu, Hani Jamjoom, Dong Su, Heqing Huang, Jialong Zhang, Tengfei Ma, Dimitrios Pendarakis, Ian Molloy
We also demonstrate that when malicious training participants tend to implant backdoors during model training, CALTRAIN can accurately and precisely discover the poisoned and mislabeled training data that lead to the runtime mispredictions.
no code implementations • EACL 2017 • Long Duong, Hiroshi Kanayama, Tengfei Ma, Steven Bird, Trevor Cohn
Crosslingual word embeddings represent lexical items from different languages using the same vector space, enabling crosslingual transfer.
no code implementations • ICLR 2019 • Tengfei Ma, Cao Xiao, Junyuan Shang, Jimeng Sun
By integrating the conditional random fields (CRF) in the graph convolutional networks, we explicitly model a joint probability of the entire set of node labels, thus taking advantage of neighborhood label information in the node label prediction task.
no code implementations • 17 Jul 2019 • Yong Wang, Zhihua Jin, Qianwen Wang, Weiwei Cui, Tengfei Ma, Huamin Qu
Node-link diagrams are widely used to facilitate network explorations.
no code implementations • 4 Oct 2019 • Tengfei Ma, Junyuan Shang, Cao Xiao, Jimeng Sun
We propose the graph energy neural network (GENN) to explicitly model link type correlations.
no code implementations • WS 2019 • Siyu Huo, Tengfei Ma, Jie Chen, Maria Chang, Lingfei Wu, Michael Witbrock
Semantic parsing is a fundamental problem in natural language understanding, as it involves the mapping of natural language to structured forms such as executable queries or logic-like knowledge representations.
no code implementations • IJCNLP 2019 • Chul Sung, Tejas Dhamecha, Swarnadeep Saha, Tengfei Ma, Vinay Reddy, Rishi Arora
Pre-trained BERT contextualized representations have achieved state-of-the-art results on multiple downstream NLP tasks by fine-tuning with task-specific data.
no code implementations • 21 May 2020 • Cao Xiao, Trong Nghia Hoang, Shenda Hong, Tengfei Ma, Jimeng Sun
There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in rich-data environments (e. g., intensive care units).
no code implementations • 21 May 2020 • Xiangxiang Zeng, Xiang Song, Tengfei Ma, Xiaoqin Pan, Yadi Zhou, Yuan Hou, Zheng Zhang, George Karypis, Feixiong Cheng
While this study, by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential of accelerating therapeutic development for COVID-19.
no code implementations • ICLR 2020 • Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen
Graph-structured data is prevalent in many domains.
no code implementations • 1 Jan 2021 • Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji
The proposed MGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two graphs.
no code implementations • 1 Jan 2021 • Ze Ye, Tengfei Ma, Chien-Chun Ni, Kin Sum Liu, Jie Gao, Chao Chen
We propose a novel GNN defense algorithm against structural attacks that maliciously modify graph topology.
no code implementations • 25 Oct 2020 • Hanlu Wu, Tengfei Ma, Lingfei Wu, Shouling Ji
Besides, we exploit the unknown latent interaction between the same type of nodes (workers or tasks) by adding a homogeneous attention layer in the graph neural networks.
no code implementations • 24 Oct 2020 • Xiang Ling, Lingfei Wu, Saizhuo Wang, Gaoning Pan, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji
To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet.
no code implementations • 22 Nov 2020 • Zhihua Jin, Yong Wang, Qianwen Wang, Yao Ming, Tengfei Ma, Huamin Qu
Two case studies and interviews with domain experts demonstrate the effectiveness of GNNLens in facilitating the understanding of GNN models and their errors.
no code implementations • 14 Jan 2021 • Zhixian Chen, Tengfei Ma, Zhihua Jin, Yangqiu Song, Yang Wang
Graph convolutional networks (GCNs) have achieved great success on graph-structured data.
no code implementations • 6 Feb 2021 • Zhixian Chen, Tengfei Ma, Yangqiu Song, Yang Wang
In this paper, we propose an innovative node representation learning framework, Wasserstein Graph Neural Network (WGNN), to mitigate the problem.
no code implementations • 28 May 2021 • Arthur Feeney, Rishabh Gupta, Veronika Thost, Rico Angell, Gayathri Chandu, Yash Adhikari, Tengfei Ma
Sampling is an established technique to scale graph neural networks to large graphs.
no code implementations • 29 Sep 2021 • Zhixian Chen, Tengfei Ma, Yang Wang
We show that the homophily degree of graphs significantly affects the prediction error of graph filters.
no code implementations • 29 Sep 2021 • EunJeong Hwang, Veronika Thost, Shib Sankar Dasgupta, Tengfei Ma
It is well known that the graph classification performance of graph neural networks often improves by adding an artificial virtual node to the graphs, which is connected to all nodes in the graph.
no code implementations • 29 Sep 2021 • Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen
We propose to collect cycle bases that span the space of cycles.
no code implementations • 29 Sep 2021 • Tengfei Ma, Trong Nghia Hoang, Jie Chen
On the top is a federation of the local data representations, performing global inference that incorporates all distributed parts collectively.
no code implementations • 25 Sep 2019 • Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Chunming Wu, Shouling Ji
The proposed HGMN model consists of a multi-perspective node-graph matching network for effectively learning cross-level interactions between parts of a graph and a whole graph, and a siamese graph neural network for learning global-level interactions between two graphs.
no code implementations • 16 Feb 2022 • Zhixian Chen, Tengfei Ma, Yang Wang
Although graph filters provide theoretical foundations for model explanations, it is unclear when a spectral GNN will fail.
no code implementations • 21 May 2022 • Yangkai Du, Tengfei Ma, Lingfei Wu, Yiming Wu, Xuhong Zhang, Bo Long, Shouling Ji
Towards real-world information extraction scenario, research of relation extraction is advancing to document-level relation extraction(DocRE).
Ranked #25 on Relation Extraction on DocRED
no code implementations • 9 Aug 2022 • Weimin Lyu, Songzhu Zheng, Tengfei Ma, Haibin Ling, Chao Chen
Trojan attacks pose a severe threat to AI systems.
no code implementations • 15 Sep 2022 • Ruixuan Yan, Tengfei Ma, Achille Fokoue, Maria Chang, Agung Julius
In this study, we present Neuro-Symbolic Time Series Classification (NSTSC), a neuro-symbolic model that leverages signal temporal logic (STL) and neural network (NN) to accomplish TSC tasks using multi-view data representation and expresses the model as a human-readable, interpretable formula.
no code implementations • 9 May 2023 • Yinan Liu, Xinyu Dong, Weimin Lyu, Richard N. Rosenthal, Rachel Wong, Tengfei Ma, Fusheng Wang
Class imbalance problems widely exist in the medical field and heavily deteriorates performance of clinical predictive models.
no code implementations • 31 Aug 2023 • Xiang Li, Shunpan Liang, Yulei Hou, Tengfei Ma
After that, we design a pyramid-like stratification method based on relevance to strengthen the expressiveness of sparse data.
no code implementations • 13 Nov 2023 • Yangkai Du, Tengfei Ma, Lingfei Wu, Xuhong Zhang, Shouling Ji
Code Clone Detection, which aims to retrieve functionally similar programs from large code bases, has been attracting increasing attention.
no code implementations • 9 Dec 2023 • Tengfei Ma, Yujie Chen, Wen Tao, Dashun Zheng, Xuan Lin, Patrick Cheong-lao Pang, Yiping Liu, Yijun Wang, Bosheng Song, Xiangxiang Zeng
By maximizing the mutual information between the reliable structure and smoothed semantic relations, DenoisedLP emphasizes the informative interactions for predicting relation-specific links.
no code implementations • 1 Mar 2024 • Shunpan Liang, Xiang Li, Chen Li, Yu Lei, Yulei Hou, Tengfei Ma
Medication recommendation aims to integrate patients' long-term health records with medical knowledge, recommending accuracy and safe medication combinations for specific conditions.
no code implementations • 5 Apr 2024 • Tengfei Ma, Xiang Song, Wen Tao, Mufei Li, Jiani Zhang, Xiaoqin Pan, Jianxin Lin, Bosheng Song, Xiangxiang Zeng
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web.
no code implementations • 18 Apr 2024 • Xiang Li, Shunpan Liang, Yu Lei, Chen Li, Yulei Hou, Tengfei Ma
Medication recommendation systems are designed to deliver personalized drug suggestions that are closely aligned with individual patient needs.