87 citations · 221 across the 37 of their papers we have counts for
6 papers · 1 filter
Latent Iterative Refinement Flow: A Geometric Constrained Approach for Few-Shot Generation
Songtao Li, Tianqi Hou, Zhenyu Liao +1
Diffusion and flow-matching models trained with limited data often tend to memorize the training data instead of generalization, leading to severely reduced diversity. In this pape…
NeuronSeek: On Stability and Expressivity of Task-driven Neurons
Hanyu Pei, Jing-Xiao Liao, Qibin Zhao +4
Drawing inspiration from our human brain that designs different neurons for different tasks, recent advances in deep learning have explored modifying a network's neurons to develop…
Early Warning Prediction with Automatic Labeling in Epilepsy Patients
Peng Zhang, Ting Gao, Jin Guo +2
Early warning for epilepsy patients is crucial for their safety and well-being, in particular to prevent or minimize the severity of seizures. Through the patients' EEG data, we pr…
Learning Stochastic Dynamical Systems as an Implicit Regularization with Graph Neural Networks
Jin Guo, Ting Gao, Yufu Lan +3
Stochastic Gumbel graph networks are proposed to learn high-dimensional time series, where the observed dimensions are often spatially correlated. To that end, the observed randomn…
Multi-task Meta Label Correction for Time Series Prediction
Luxuan Yang, Ting Gao, Wei Wei +3
Time series classification faces two unavoidable problems. One is partial feature information and the other is poor label quality, which may affect model performance. To address th…
Neural network stochastic differential equation models with applications to financial data forecasting
Luxuan Yang, Ting Gao, Yubin Lu +2
In this article, we employ a collection of stochastic differential equations with drift and diffusion coefficients approximated by neural networks to predict the trend of chaotic t…