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      "statement": "# FFJORD: scalable continuous normalizing flows\n\nCombined continuous-time invertible dynamics with Hutchinson trace estimation and adjoint-based training to scale log-density evaluation without triangular neural Jacobian restrictions.\n\n## Scientific scope\n\nInherits continuous normalizing flows, neural ODEs, adjoints and Hutchinson estimation. Unbiasedness concerns the stochastic log-density integral under stated regularity and exact integration; exponentiation is not thereby an unbiased density estimate and practical ODE solvers introduce numerical error. One-pass sampling is an ODE solve with potentially many function evaluations, not a single network evaluation.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nWill Grathwohl; Ricky T. Q. Chen; Jesse Bettencourt; Ilya Sutskever; David Duvenaud\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models](https://arxiv.org/abs/1810.01367v1)\n",
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