1 citations · 1 across the 7 of their papers we have counts for
7 papers
A DeepLagrangian method for learning and generating aggregation patterns in multi-dimensional Keller-Segel chemotaxis systems
Yani Feng, Michael K. Ng, Zhiwen Zhang
The Keller-Segel (KS) chemotaxis system is used to describe the overall behavior of a collection of cells under the influence of chemotaxis. However, solving the KS chemotaxis syst…
Functional tensor train neural network for solving high-dimensional PDEs
Yani Feng, Michael K. Ng, Kejun Tang +1
Discrete tensor train decomposition is widely employed to mitigate the curse of dimensionality in solving high-dimensional PDEs through traditional methods. However, the direct app…
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning
Xue Yang, Depan Peng, Yan Feng +3
Bidirectional privacy-preservation federated learning is crucial as both local gradients and the global model may leak privacy. However, only a few works attempt to achieve it, and…
Dimension-reduced KRnet maps for high-dimensional Bayesian inverse problems
Yani Feng, Kejun Tang, Xiaoliang Wan +1
We present a dimension-reduced KRnet map approach (DR-KRnet) for high-dimensional Bayesian inverse problems, which is based on an explicit construction of a map that pushes forward…
Streaming data recovery via Bayesian tensor train decomposition
Yunyu Huang, Yani Feng, Qifeng Liao
In this paper, we study a Bayesian tensor train (TT) decomposition method to recover streaming data by approximating the latent structure in high-order streaming data. Drawing on t…
Tensor Train Random Projection
Yani Feng, Kejun Tang, Lianxing He +2
This work proposes a novel tensor train random projection (TTRP) method for dimension reduction, where pairwise distances can be approximately preserved. Our TTRP is systematically…
High order tensor moments of random vectors
Yan Feng, Shan Song, Changqing Xu
A random vector $\bx\in \R^n$ is a vector whose coordinates are all random variables. A random vector is called a Gaussian vector if it follows Gaussian distribution. These termino…