10 citations · 11 across the 5 of their papers we have counts for
5 papers
Rethinking Nonlinearity: Trainable Gaussian Mixture Modules for Modern Neural Architectures
Weiguo Lu, Gangnan Yuan, Hong-kun Zhang +1
Neural networks in general, from MLPs and CNNs to attention-based Transformers, are constructed from layers of linear combinations followed by nonlinear operations such as ReLU, Si…
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient
Weiguo Lu, Xuan Wu, Deng Ding +3
Diffusion models (DMs) are a type of generative model that has a huge impact on image synthesis and beyond. They achieve state-of-the-art generation results in various generative t…
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options
Jirong Zhuang, Deng Ding, Weiguo Lu +2
In this work, we present a novel machine learning approach for pricing high-dimensional American options based on the modified Gaussian process regression (GPR). We incorporate dee…
An Efficient 1 Iteration Learning Algorithm for Gaussian Mixture Model And Gaussian Mixture Embedding For Neural Network
Weiguo Lu, Xuan Wu, Deng Ding +1
We propose an Gaussian Mixture Model (GMM) learning algorithm, based on our previous work of GMM expansion idea. The new algorithm brings more robustness and simplicity than classi…
Total value adjustment of Bermudan option valuation under pure jump Lévy fluctuations
Gangnan Yuan, Ding Deng, Jinqiao Duan +2
During the COVID-19 pandemic, many institutions have announced that their counterparties are struggling to fulfill contracts.Therefore, it is necessary to consider the counterparty…