Domain transfer becomes identifiable from marginals plus one anchor under Jacobian sparsity, enabled by a randomized masked finite-difference regularizer.
Nature methods , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
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A generative transfer framework using iterative path-wise tilting integrated with conditional flow matching recovers target entropic optimal transport couplings from reference samples, achieving O(δ) convergence in Wasserstein-1 distance.
A single-network fixed-point formulation for neural optimal transport eliminates adversarial min-max optimization and implicit differentiation while enforcing dual feasibility exactly.
Unbalanced Schrödinger Bridge (USB) provides a tractable, simulation-free solution to the Branching Schrödinger Bridge problem for modeling discrete birth-death dynamics at single-cell resolution from snapshot data.
citing papers explorer
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Domain Transfer Becomes Identifiable via a Single Alignment
Domain transfer becomes identifiable from marginals plus one anchor under Jacobian sparsity, enabled by a randomized masked finite-difference regularizer.
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Generative Transfer for Entropic Optimal Transport with Unknown Costs
A generative transfer framework using iterative path-wise tilting integrated with conditional flow matching recovers target entropic optimal transport couplings from reference samples, achieving O(δ) convergence in Wasserstein-1 distance.
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Fixed-Point Neural Optimal Transport without Implicit Differentiation
A single-network fixed-point formulation for neural optimal transport eliminates adversarial min-max optimization and implicit differentiation while enforcing dual feasibility exactly.
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Beyond Continuity: Simulation-free Reconstruction of Discrete Branching Dynamics from Single-cell Snapshots
Unbalanced Schrödinger Bridge (USB) provides a tractable, simulation-free solution to the Branching Schrödinger Bridge problem for modeling discrete birth-death dynamics at single-cell resolution from snapshot data.