RICA replaces ICA's global generative model with local Riemannian geometry, introducing a disentanglement tensor based on the Hessian of the log-likelihood and Ricci curvature to measure pointwise disentanglement, which recovers sources across manifolds in controlled tests.
Riemannian metric learning: Closer to you than you imagine
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Starfield uses a traffic-derived Riemannian metric on the satellite shell to select demand-aware ISLs, yielding up to 30% fewer hops and 15% better stretch than grid topologies in Starlink Phase 1 simulations.
Iso-Riemannian descent algorithm with convergence analysis under iso-convexity, iso-monotonicity and iso-Lipschitz conditions for optimization on learned Riemannian manifolds from data.
citing papers explorer
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Disentanglement Beyond Generative Models with Riemannian ICA
RICA replaces ICA's global generative model with local Riemannian geometry, introducing a disentanglement tensor based on the Hessian of the log-likelihood and Ricci curvature to measure pointwise disentanglement, which recovers sources across manifolds in controlled tests.
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Starfield: Demand-Aware Satellite Topology Design for Low-Earth Orbit Mega Constellations
Starfield uses a traffic-derived Riemannian metric on the satellite shell to select demand-aware ISLs, yielding up to 30% fewer hops and 15% better stretch than grid topologies in Starlink Phase 1 simulations.
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Iso-Riemannian Optimization on Learned Data Manifolds
Iso-Riemannian descent algorithm with convergence analysis under iso-convexity, iso-monotonicity and iso-Lipschitz conditions for optimization on learned Riemannian manifolds from data.