90 citations · 155 across the 8 of their papers we have counts for
8 papers
A spectral method for a Fokker-Planck equation in neuroscience with applications in neural networks with learning rules
Pei Zhang, Yanli Wang, Zhennan Zhou
In this work, we consider the Fokker-Planck equation of the Nonlinear Noisy Leaky Integrate-and-Fire (NNLIF) model for neuron networks. Due to the firing events of neurons at the m…
Solving Boltzmann equation with neural sparse representation
Zhengyi Li, Yanli Wang, Hongsheng Liu +2
We consider the neural sparse representation to solve Boltzmann equation with BGK and quadratic collision model, where a network-based ansatz that can approximate the distribution…
Hermite spectral method for the inelastic Boltzmann equation
Ruo Li, Yixiao Lu, Yanli Wang
We propose a Hermite spectral method for the inelastic Boltzmann equation, which makes two-dimensional periodic problem computation affordable by the hardware nowadays. The new alg…
Highly efficient energy-conserving moment method for the multi-dimensional Vlasov-Maxwell system
Tianai Yin, Xinghui Zhong, Yanli Wang
We present an energy-conserving numerical scheme to solve the Vlasov-Maxwell (VM) system based on the regularized moment method proposed in [Z. Cai, Y. Fan, and R. Li. CPAM, 2014].…
LibDB: An Effective and Efficient Framework for Detecting Third-Party Libraries in Binaries
Wei Tang, Yanlin Wang, Hongyu Zhang +3
Third-party libraries (TPLs) are reused frequently in software applications for reducing development cost. However, they could introduce security risks as well. Many TPL detection…
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation
Ruixuan Liu, Yanlin Wang, Yang Cao +4
Collecting and training over sensitive personal data raise severe privacy concerns in personalized recommendation systems, and federated learning can potentially alleviate the prob…
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices
Ruixuan Liu, Fangzhao Wu, Chuhan Wu +4
Federated learning (FL) is an important paradigm for training global models from decentralized data in a privacy-preserving way. Existing FL methods usually assume the global model…
Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction
Chuhan Wu, Fangzhao Wu, Tao Qi +4
Vertical federated learning (VFL) aims to train models from cross-silo data with different feature spaces stored on different platforms. Existing VFL methods usually assume all dat…