47 citations · 106 across the 22 of their papers we have counts for
6 papers · 1 filter
Neural Network Approximation for Pessimistic Offline Reinforcement Learning
Di Wu, Yuling Jiao, Li Shen +2
Deep reinforcement learning (RL) has shown remarkable success in specific offline decision-making scenarios, yet its theoretical guarantees are still under development. Existing wo…
Sampling via Föllmer Flow
Zhao Ding, Yuling Jiao, Xiliang Lu +2
We introduce a novel unit-time ordinary differential equation (ODE) flow called the preconditioned Föllmer flow, which efficiently transforms a Gaussian measure into a desired targ…
Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits
Zhan Yu, Qiuhao Chen, Yuling Jiao +4
Parameterized quantum circuits (PQCs) have emerged as a promising approach for quantum neural networks. However, understanding their expressive power in accomplishing machine learn…
Current density impedance imaging with PINNs
Chenguang Duan, Yuling Jiao, Xiliang Lu +1
In this paper, we introduce CDII-PINNs, a computationally efficient method for solving CDII using PINNs in the framework of Tikhonov regularization. This method constructs a physic…
Deep Neural Network Approximation of Composition Functions: with application to PINNs
Chenguang Duan, Yuling Jiao, Xiliang Lu +2
In this paper, we focus on approximating a natural class of functions that are compositions of smooth functions. Unlike the low-dimensional support assumption on the covariate, we…
GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
Yuling Jiao, Di Li, Xiliang Lu +2
With the recent study of deep learning in scientific computation, the Physics-Informed Neural Networks (PINNs) method has drawn widespread attention for solving Partial Differentia…