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Proceedings of the AAAI Conference on Artificial Intelligence

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WeightFlow: Learning Stochastic Dynamics via Evolving Wei...
Ruikun Li, J · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Ruikun Li Shenzhen International Graduate School, Tsinghua University
  • Jiazhen Liu Department of Electronic Engineering, BNRist, Tsinghua University
  • Huandong Wang Department of Electronic Engineering, BNRist, Tsinghua University
  • Qingmin Liao Shenzhen International Graduate School, Tsinghua University
  • Yong Li Department of Electronic Engineering, BNRist, Tsinghua University

DOI:

https://doi.org/10.1609/aaai.v40i1.37029

Abstract

Modeling stochastic dynamics from discrete observations is a key interdisciplinary challenge. Existing methods often fail to estimate the continuous evolution of probability densities from trajectories or face the curse of dimensionality. To address these limitations, we presents a novel paradigm: modeling dynamics directly in the weight space of a neural network by projecting the evolving probability distribution. We first theoretically establish the connection between dynamic optimal transport in measure space and an equivalent energy functional in weight space. Subsequently, we design WeightFlow, which constructs the neural network weights into a graph and learns its evolution via a graph controlled differential equation. Experiments on interdisciplinary datasets show that WeightFlow improves performance by an average of 43.02\% over state-of-the-art methods, providing an effective and scalable solution for modeling high-dimensional stochastic dynamics.

How to Cite

Li, R., Liu, J., Wang, H., Liao, Q., & Li, Y. (2026). WeightFlow: Learning Stochastic Dynamics via Evolving Weight of Neural Network. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 641–649. https://doi.org/10.1609/aaai.v40i1.37029

Issue

Section

AAAI Technical Track on Application Domains I

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