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About me
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Summary: The random initialized embeddings is equivalent to multiple a random matrix to the original feature matrix. The value of each dimension in generated embeddings are the weighted summation of values of each dimension in original features. In the random initialization, the embeddings are still distinguishable since the ‘weights’ are randomly generated and not identical. In ogb datasets with enough training data, the downstream classifier can still fit it.
Portfolio item number 1
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Portfolio item number 2
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Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework
Zhenshuo Zhang, Yun Zhu, Haizhou Shi, Siliang Tang
European Conference on Artificial Intelligence, ECAI, 2023
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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