Contrastive Multi-View Representation Learning on Graphs
Jul 12, 2020
Speakers
Kaveh Hassani
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Amir Hosein Khasahmadi
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About
We introduce a model for self-supervised representation learning on both node and graph level based on InfoMax by maximizing the mutual information between spatial and spectral views of a graph. We evaluate our model on three node classification and five graph classification benchmarks and show that it achieves state-of-the-art results on all datasets and narrows the gap between supervised and unsupervised models.We introduce a model for self-supervised representation learning on both node and graph level based on InfoMax by maximizing the mutual information between spatial and spectral views of a graph. We evaluate our model on three node classification and five graph classification benchmarks and show that it achieves state-of-the-art results on all datasets and narrows the gap between supervised and unsupervised models.…
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Mathematics
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AI & Data Science
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About ICML 2020
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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