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New inductive framework for Gromov-Wasserstein embeddings unveiled

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new inductive framework for Gromov-Wasserstein multidimensional scaling (GW-MDS) that allows for the mapping of unseen samples. This approach, termed barycentric distillation, uses a teacher model to learn latent representations and optimal transport plans from training data. A student neural network then learns an explicit out-of-sample mapping by utilizing barycentric projection, which effectively bridges transductive GW embeddings with inductive neural mappings. Experiments demonstrate that this distilled model preserves the teacher's geometry on new data and outperforms direct neural GW training. AI

IMPACT This new inductive framework could improve the efficiency and applicability of relational data embedding in machine learning tasks.

RANK_REASON The cluster contains a research paper detailing a new method for multi-view embedding. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New inductive framework for Gromov-Wasserstein embeddings unveiled

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The cluster contains a research paper detailing a new method for multi-view embedding. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante ·

    Gromov-Wasserstein Distillation for Inductive Multi-View Embedding

    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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