70 citations · 164 across the 35 of their papers we have counts for
7 papers · 1 filter
Bayesian Inversion with Neural Operator (BINO) for Modeling Subdiffusion: Forward and Inverse Problems
Xiong-bin Yan, Zhi-Qin John Xu, Zheng Ma
Fractional diffusion equations have been an effective tool for modeling anomalous diffusion in complicated systems. However, traditional numerical methods require expensive computa…
Linear Stability Hypothesis and Rank Stratification for Nonlinear Models
Yaoyu Zhang, Zhongwang Zhang, Leyang Zhang +3
Models with nonlinear architectures/parameterizations such as deep neural networks (DNNs) are well known for their mysteriously good generalization performance at overparameterizat…
DeepFlame: A deep learning empowered open-source platform for reacting flow simulations
Runze Mao, Minqi Lin, Yan Zhang +3
In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows.…
Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks
Zhiwei Bai, Tao Luo, Zhi-Qin John Xu +1
Understanding the relation between deep and shallow neural networks is extremely important for the theoretical study of deep learning. In this work, we discover an embedding princi…
An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation
Shuyu Yin, Tao Luo, Peilin Liu +1
Gradient descent or its variants are popular in training neural networks. However, in deep Q-learning with neural network approximation, a type of reinforcement learning, gradient…
Limitation of Characterizing Implicit Regularization by Data-independent Functions
Leyang Zhang, Zhi-Qin John Xu, Tao Luo +1
In recent years, understanding the implicit regularization of neural networks (NNs) has become a central task in deep learning theory. However, implicit regularization is itself no…
Overview frequency principle/spectral bias in deep learning
Zhi-Qin John Xu, Yaoyu Zhang, Tao Luo
Understanding deep learning is increasingly emergent as it penetrates more and more into industry and science. In recent years, a research line from Fourier analysis sheds lights o…