Condensing graphs via one-step gradient matching

W Jin, X Tang, H Jiang, Z Li, D Zhang, J Tang… - Proceedings of the 28th …, 2022 - dl.acm.org
As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …
被引用次数：83 相关文章
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[HTML][HTML] Condensing graphs via one-step gradient matching

W Jin, X Tang, H Jiang, Z Li, DD Zhang, J Tang, B Ying - 2022 - amazon.science
Graph-structured data plays a key role in various real-world applications. For example, by
exploiting graph structural information, we can predict the chemical property of a given …

Condensing Graphs via One-Step Gradient Matching

W Jin, X Tang, H Jiang, Z Li, D Zhang, J Tang… - NeurIPS 2022 Workshop … - openreview.net
As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …

Condensing Graphs via One-Step Gradient Matching

W Jin, X Tang, H Jiang, Z Li, D Zhang, J Tang… - arXiv preprint arXiv …, 2022 - arxiv.org
As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …

Condensing Graphs via One-Step Gradient Matching

W Jin, X Tang, H Jiang, Z Li, D Zhang, J Tang… - arXiv e …, 2022 - ui.adsabs.harvard.edu
As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …
[HTML] amazon.science

[HTML][HTML] Condensing graphs via one-step gradient matching

W Jin, X Tang, H Jiang, Z Li, DD Zhang, J Tang, B Ying… - amazon.science
As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …