TriOpt recovers topological ordering via Sherman-Morrison rank-1 updates on linear kernels and then solves a convex continuous optimization for the linear DAG structure.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A parametric autoencoder with non-negativity and softmax constraints learns interpretable latent chemical components and couples them to kinetics and heat release for improved reduced-order modeling of decomposition.
citing papers explorer
-
TriOpt: A Scalable Algorithm for Linear Causal Discovery
TriOpt recovers topological ordering via Sherman-Morrison rank-1 updates on linear kernels and then solves a convex continuous optimization for the linear DAG structure.
-
A Data-Driven Parametric Reduced-Order Chemical Kinetics Model Derived from Atomistic Simulations
A parametric autoencoder with non-negativity and softmax constraints learns interpretable latent chemical components and couples them to kinetics and heat release for improved reduced-order modeling of decomposition.