776 citations
- Université de MontréalCA16 papers
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- Mila - Quebec Artificial Intelligence InstituteCA9 papers
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- Polytechnique MontréalCA7 papers
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11 papers · 1 filter
Continuous Graph Neural Networks
Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang
This paper builds on the connection between graph neural networks and traditional dynamical systems. We propose continuous graph neural networks (CGNN), which generalise existing g…
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu +4
Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relat…
Structural Robustness for Deep Learning Architectures
Carlos Lassance, Vincent Gripon, Jian Tang +1
Deep Networks have been shown to provide state-of-the-art performance in many machine learning challenges. Unfortunately, they are susceptible to various types of noise, including…
Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures
Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada +3
Generative models have achieved impressive results in many domains including image and text generation. In the natural sciences, generative models have led to rapid progress in aut…
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma +1
This paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph-level representations are critical in a variety of real-wo…
Probabilistic Logic Neural Networks for Reasoning
Meng Qu, Jian Tang
Knowledge graph reasoning, which aims at predicting the missing facts through reasoning with the observed facts, is critical to many applications. Such a problem has been widely ex…