10 citations · 21 across the 4 of their papers we have counts for
5 papers
Scalable Levenberg-Marquardt Training Framework for Deep Learning
Xianghao Wang, Johan Barthélemy, John Taylor
The Levenberg-Marquardt (LMA) algorithm is a widely used nonlinear least-squares solver known for its rapid convergence. For small-scale neural networks, LMA provides better conver…
Machine Learning based Parameter Sensitivity of Regional Climate Models -- A Case Study of the WRF Model for Heat Extremes over Southeast Australia
P. Jyoteeshkumar Reddy, Sandeep Chinta, Richard Matear +5
Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionab…
Optimizing the optimizer for data driven deep neural networks and physics informed neural networks
John Taylor, Wenyi Wang, Biswajit Bala +1
We investigate the role of the optimizer in determining the quality of the model fit for neural networks with a small to medium number of parameters. We study the performance of Ad…
A Deep Learning Model for Forecasting Global Monthly Mean Sea Surface Temperature Anomalies
John Taylor, Ming Feng
Sea surface temperature (SST) variability plays a key role in the global weather and climate system, with phenomena such as El Niño-Southern Oscillation regarded as a major source…
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series
Wenyi Wang, John Taylor, Biswajit Bala
The Levenberg-Marquardt (LM) optimization algorithm has been widely used for solving machine learning problems. Literature reviews have shown that the LM can be very powerful and e…