DeFed-GMM-DaDiL enables stable decentralized domain adaptation by approximating client GMMs through shared learnable atoms and labeled Wasserstein barycenters, reconstructing missing classes competitively.
Optimal Transport for Domain Adaptation , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
GTSA-PCA replaces global PCA covariance with curvature-weighted local operators and a geodesic alignment step to produce geometry-aware embeddings that improve on standard PCA and UMAP in small-sample high-curvature settings.
citing papers explorer
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DeFed-GMM-DaDiL: A Decentralized Federated Framework for Domain Adaptation
DeFed-GMM-DaDiL enables stable decentralized domain adaptation by approximating client GMMs through shared learnable atoms and labeled Wasserstein barycenters, reconstructing missing classes competitively.
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Curvature-Aware PCA with Geodesic Tangent Space Aggregation for Semi-Supervised Learning
GTSA-PCA replaces global PCA covariance with curvature-weighted local operators and a geodesic alignment step to produce geometry-aware embeddings that improve on standard PCA and UMAP in small-sample high-curvature settings.