11 citations · 25 across the 15 of their papers we have counts for
5 papers · 1 filter
Feasibility of Transfer Learning: A Mathematical Framework
Haoyang Cao, Haotian Gu, Xin Guo
Transfer learning is a popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. It has enjoyed numerous empirical succ…
Feasibility and Transferability of Transfer Learning: A Mathematical Framework
Haoyang Cao, Haotian Gu, Xin Guo +1
Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. Despite its numerous emp…
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system
Haoyang Cao, Xin Guo, Guan Wang
Anomaly detection has been an active research area with a wide range of potential applications. Key challenges for anomaly detection in the AI era with big data include lack of pri…
Adversarial Training for Gradient Descent: Analysis Through its Continuous-time Approximation
Haotian Gu, Xin Guo, Xinyu Li
Adversarial training has gained great popularity as one of the most effective defenses for deep neural network and more generally for gradient-based machine learning models against…
SDE approximations of GANs training and its long-run behavior
Haoyang Cao, Xin Guo
This paper analyzes the training process of GANs via stochastic differential equations (SDEs). It first establishes SDE approximations for the training of GANs under stochastic gra…