Defines generative probability paths as self-consistent when they form random fixed points of admissible variational transport operators and derives associated existence, attraction, and residual bounds.
arXiv preprint arXiv:2402.06162 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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A single-network implicit neural optimal transport method that solves the c-transform via proximal fixed-point iteration for stable, non-adversarial training.
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Self-Consistent Generative Paths via Admissible Random Variational Transport
Defines generative probability paths as self-consistent when they form random fixed points of admissible variational transport operators and derives associated existence, attraction, and residual bounds.
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Implicit Neural Optimal Transport via Fixed-Point Optimization
A single-network implicit neural optimal transport method that solves the c-transform via proximal fixed-point iteration for stable, non-adversarial training.
- Robustness and Structure Preservation in Flow-Based Generative Models via Wasserstein Path-Space Divergences