English(EN) Gromov-Wasserstein Distillation for Inductive Multi-View Embedding

公布了用于 Gromov-Wasserstein 嵌入的新归纳式框架

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了一种新的 Gromov-Wasserstein 多维标度 (GW-MDS) 归纳式框架，该框架允许映射未见过的样本。这种方法称为重心蒸馏，它使用教师模型从训练数据中学习潜在表示和最优传输计划。然后，学生神经网络通过利用重心投影来学习显式的样本外映射，从而有效地将传导式 GW 嵌入与归纳式神经网络映射联系起来。实验表明，这种蒸馏模型在新数据上保留了教师的几何结构，并且优于直接的神经网络 GW 训练。 AI

影响这种新的归纳式框架可以提高机器学习任务中关系数据嵌入的效率和适用性。

排序理由该集群包含一篇详细介绍多视图嵌入新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante · 2026-10-01 04:00

用于归纳式多视图嵌入的Gromov-Wasserstein蒸馏

arXiv:2609.40047v1 Announce Type: cross Abstract: Gromov-Wasserstein multidimensional scaling (GW-MDS) learns low-dimensional representations from relational data but remains transductive, providing no explicit mapping for unseen samples. We introduce an inductive framework based…

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报道来源 [1]

用于归纳式多视图嵌入的Gromov-Wasserstein蒸馏

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