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Meng Qu

Alumni

Publications

COVI White Paper-Version 1.1
H. Alsdurf
Yoshua Bengio
T. Deleu
Prateek Gupta
Daphne Ippolito
R. Janda
Max Jarvie
Tyler Kolody
S. Krastev
Tegan Maharaj
Robert Obryk
D. Pilat
Valérie Pisano
Benjamin Prud'homme
Meng Qu
Nasim Rahaman
I. Rish
J. Rousseau
Abhinav Sharma
B. Struck … (voir 3 de plus)
Jian Tang
Martin Weiss
Yun William Yu
2019-12-31
(publié)
www.semanticscholar.org
GraphMix: Improved Training of Graph Neural Networks for Semi-Supervised Learning
Vikas Verma
Meng Qu
Alex Lamb
Yoshua Bengio
Juho Kannala
Jian Tang
We present GraphMix , a regularized training scheme for Graph Neural Network based semi-supervised object classification, leveraging the re… (voir plus)cent advances in the regularization of classical deep neural networks. Specifically, we pro-pose a unified approach in which we train a fully-connected network jointly with the graph neural network via parameter sharing, interpolation-based regularization and self-predicted-targets. Our proposed method is architecture agnostic in the sense that it can be applied to any variant of graph neural networks which applies a parametric transformation to the features of the graph nodes. Despite its simplicity, with GraphMix we can consistently improve results and achieve or closely match state-of-the-art performance using even simpler architectures such as Graph Convolutional Networks, across three established graph benchmarks: Cora, Citeseer and Pubmed citation network datasets, as well as three newly proposed datasets :Cora-Full, Co-author-CS and Co-author-Physics.
2019-12-31
(publié)
www.semanticscholar.org
Weakly-supervised Knowledge Graph Alignment with Adversarial Learning
Meng Qu
Jian Tang
Yoshua Bengio
This paper studies aligning knowledge graphs from different sources or languages. Most existing methods train supervised methods for the ali… (voir plus)gnment, which usually require a large number of aligned knowledge triplets. However, such a large number of aligned knowledge triplets may not be available or are expensive to obtain in many domains. Therefore, in this paper we propose to study aligning knowledge graphs in fully-unsupervised or weakly-supervised fashion, i.e., without or with only a few aligned triplets. We propose an unsupervised framework to align the entity and relation embddings of different knowledge graphs with an adversarial learning framework. Moreover, a regularization term which maximizes the mutual information between the embeddings of different knowledge graphs is used to mitigate the problem of mode collapse when learning the alignment functions. Such a framework can be further seamlessly integrated with existing supervised methods by utilizing a limited number of aligned triples as guidance. Experimental results on multiple datasets prove the effectiveness of our proposed approach in both the unsupervised and the weakly-supervised settings.
2019-07-05
ArXiv (prépublication)
openreview.net
openreview.net
GMNN: Graph Markov Neural Networks
Meng Qu
Yoshua Bengio
Jian Tang
This paper studies semi-supervised object classification in relational data, which is a fundamental problem in relational data modeling. The… (voir plus) problem has been extensively studied in the literature of both statistical relational learning (e.g. relational Markov networks) and graph neural networks (e.g. graph convolutional networks). Statistical relational learning methods can effectively model the dependency of object labels through conditional random fields for collective classification, whereas graph neural networks learn effective object representations for classification through end-to-end training. In this paper, we propose the Graph Markov Neural Network (GMNN) that combines the advantages of both worlds. A GMNN models the joint distribution of object labels with a conditional random field, which can be effectively trained with the variational EM algorithm. In the E-step, one graph neural network learns effective object representations for approximating the posterior distributions of object labels. In the M-step, another graph neural network is used to model the local label dependency. Experiments on object classification, link classification, and unsupervised node representation learning show that GMNN achieves state-of-the-art results.
2019-05-23
Proceedings of the 36th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press