An n-dimensional hybrid system embeds into a continuous vector field in m > 2n dimensions, enabling latent Neural ODEs with consistency losses to recover hybrid flows from time series.
arXiv preprint arXiv:2101.12490 , year=
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
Score Kalman Filter performs nonlinear moment-based filtering by reducing density fitting to a linear solve from moments via score matching and closing hierarchies with Stein's identity, avoiding partition function integrals entirely.
A novel Sum-of-Squares form for conditional density estimation in Markov processes enables analytical belief propagation with exact constraint adherence and better scaling than prior methods.
LieIPM applies a structure-preserving interior point optimizer to rigid-body trajectory planning on Lie groups using variational integrators and closed-form intrinsic derivatives.
citing papers explorer
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Embedding Hybrid Systems into Continuous Latent Vector Fields
An n-dimensional hybrid system embeds into a continuous vector field in m > 2n dimensions, enabling latent Neural ODEs with consistency losses to recover hybrid flows from time series.
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The Score Kalman Filter
Score Kalman Filter performs nonlinear moment-based filtering by reducing density fitting to a linear solve from moments via score matching and closing hierarchies with Stein's identity, avoiding partition function integrals entirely.
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Learning Markov Processes as Sum-of-Square Forms for Analytical Belief Propagation
A novel Sum-of-Squares form for conditional density estimation in Markov processes enables analytical belief propagation with exact constraint adherence and better scaling than prior methods.
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LieIPM: Lie Group Interior Point Method for Direct Trajectory Optimization of Rigid Bodies
LieIPM applies a structure-preserving interior point optimizer to rigid-body trajectory planning on Lie groups using variational integrators and closed-form intrinsic derivatives.