5 citations · 13 across the 5 of their papers we have counts for
5 papers
Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting
Yiqi Sun, Zhengxin Shi, Jianshen Zhang +3
In time series forecasting, decomposition-based algorithms break aggregate data into meaningful components and are therefore appreciated for their particular advantages in interpre…
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information
Majid Jahani, Sergey Rusakov, Zheng Shi +3
We present a novel adaptive optimization algorithm for large-scale machine learning problems. Equipped with a low-cost estimate of local curvature and Lipschitz smoothness, our met…
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods
Zheng Shi, Abdurakhmon Sadiev, Nicolas Loizou +2
We present AI-SARAH, a practical variant of SARAH. As a variant of SARAH, this algorithm employs the stochastic recursive gradient yet adjusts step-size based on local geometry. AI…
Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations
Zheng Shi, Nur Sila Gulgec, Albert S. Berahas +2
Discovering the underlying behavior of complex systems is an important topic in many science and engineering disciplines. In this paper, we propose a novel neural network framework…
FD-Net with Auxiliary Time Steps: Fast Prediction of PDEs using Hessian-Free Trust-Region Methods
Nur Sila Gulgec, Zheng Shi, Neil Deshmukh +2
Discovering the underlying physical behavior of complex systems is a crucial, but less well-understood topic in many engineering disciplines. This study proposes a finite-differenc…