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Flow matching achieves minimax optimal convergence
Flow matching (FM) has gained significant attention as a simulation-free generative model.
Extended Flow Matching: a Method of Conditional Generation with Generalized Continuity Equation
We show that we can introduce inductive bias to the conditional generation through the matrix field and demonstrate this fact with MMOT-EFM, a version of EFM that aims to minimize the Dirichlet energy or the sensitivity of the distribution with respect to conditions.
A convergence result of a continuous model of deep learning via Łojasiewicz--Simon inequality
This study focuses on a Wasserstein-type gradient flow, which represents an optimization process of a continuous model of a Deep Neural Network (DNN).
Variational formulations of ODE-Net as a mean-field optimal control problem and existence results
This paper presents a mathematical analysis of ODE-Net, a continuum model of deep neural networks (DNNs).
