32 citations · 125 across the 15 of their papers we have counts for
5 papers · 1 filter
Structure-Aware Variational Learning of a Class of Generalized Diffusions
Yubin Lu, Xiaofan Li, Chun Liu +2
Learning the underlying potential energy of stochastic gradient systems from partial and noisy observations is a fundamental problem arising in physics, chemistry, and data-driven…
Moment Estimates and DeepRitz Methods on Learning Diffusion Systems with Non-gradient Drifts
Fanze Kong, Chen-Chih Lai, Yubin Lu
Conservative-dissipative dynamics are ubiquitous across a variety of complex open systems. We propose a data-driven two-phase method, the Moment-DeepRitz Method, for learning drift…
Multi-task Meta Label Correction for Time Series Prediction
Luxuan Yang, Ting Gao, Wei Wei +3
Time series classification faces two unavoidable problems. One is partial feature information and the other is poor label quality, which may affect model performance. To address th…
Mathematical analysis of singularities in the diffusion model under the submanifold assumption
Yubin Lu, Zhongjian Wang, Guillaume Bal
This paper concerns the mathematical analyses of the diffusion model in machine learning. The drift term of the backward sampling process is represented as a conditional expectatio…
Neural network stochastic differential equation models with applications to financial data forecasting
Luxuan Yang, Ting Gao, Yubin Lu +2
In this article, we employ a collection of stochastic differential equations with drift and diffusion coefficients approximated by neural networks to predict the trend of chaotic t…