NS-RGS: Newton-Schulz based Riemannian gradient method for orthogonal group synchronization
arXiv 2604.07372•c283f1377afe7b7f900bf10daf6d7d22c1df7da40c00e9b27a5600ce8c465628
1-bit-mean-estimationGPU-accelerationICARNewton-SchulzPDE-inverse-problemsREMLRiemannian-optimizationclusteringdifferential-privacyelastic-netgraph-modelsidentification-in-the-limit','Hawkes-processes','parallel-computmachine-learningoptimizationorthogonal-group-synchronizationprivate-generationprototype-clusteringquantized-statisticsrandom-dot-product-graphsrobust-SVMspatial-statisticssupport-vector-machinestransport-mapsvariational-inferencevector-fields
Paper metadata
- arXiv ID
- 2604.07372
- Version
- Not specified by this published record
- Category
- Statistics — Machine Learning (stat.ML)
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- Source ID
- arxiv_stat_ml
- Record identifier
- c283f1377afe7b7f900bf10daf6d7d22c1df7da40c00e9b27a5600ce8c465628
- Enrichment time
- 2026-04-10T07:23:56Z
- AI-assisted enrichment
- Yes
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