1 citations · 1 across the 5 of their papers we have counts for
5 papers
Inversion-Free Natural Gradient Descent on Riemannian Manifolds
Dario Draca, Takuo Matsubara, Minh-Ngoc Tran
The natural gradient method is a central tool for statistical optimisation, but its broader application is hindered by the assumption of a Euclidean parameter space, the repeated e…
From Noise to Laws: Regularized Time-Series Forecasting via Denoised Dynamic Graphs
Hongwei Ma, Junbin Gao, Minh-ngoc Tran
Long-horizon multivariate time-series forecasting is challenging because realistic predictions must (i) denoise heterogeneous signals, (ii) track time-varying cross-series dependen…
Signals, Concepts, and Laws: Toward Universal, Explainable Time-Series Forecasting
Hongwei Ma, Junbin Gao, Minh-Ngoc Tran
Accurate, explainable and physically credible forecasting remains a persistent challenge for multivariate time-series whose statistical properties vary across domains. We propose D…
PREIG: Physics-informed and Reinforcement-driven Interpretable GRU for Commodity Demand Forecasting
Hongwei Ma, Junbin Gao, Minh-Ngoc Tran
Accurately forecasting commodity demand remains a critical challenge due to volatile market dynamics, nonlinear dependencies, and the need for economically consistent predictions.…
Regularized Flexible Activation Function Combinations for Deep Neural Networks
Renlong Jie, Junbin Gao, Andrey Vasnev +1
Activation in deep neural networks is fundamental to achieving non-linear mappings. Traditional studies mainly focus on finding fixed activations for a particular set of learning t…