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swethatanamala.github.io | ||
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11011110.github.io
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| | | | | Another new preprint of mine recently appeared: "Bandwidth vs BFS width in matrix reordering, graph reconstruction, and graph drawing", arXiv:2505.10789, wit... | |
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research.google
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| | | | | Posted by Bryan Perozzi, Research Scientist and Qi Zhu, Research Intern, Google Research Graph Neural Networks (GNNs) are powerful tools for levera... | |
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www.simula.no
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| | | | | Graph neural networks (GNNs), which extend the successful ideas of deep learning to irregularly structured data, are a recent addition to the field of artificial intelligence. While traditional deep learning has focused on regular inputs such as images composed of pixels in two-dimensional space, graph neural networks can analyze and learn from unstructured connections between objects. This gives GNNs the ability to tackle completely new classes of problems, such as analyzing social networks and power grids or uncovering molecule structures in computational chemistry. | |
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jakeseliger.com
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| | | 1 post published by Jake Seliger on October 9, 2012 | ||