4 citations · 8 across the 3 of their papers we have counts for
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cs.LG2023
Parallelizing non-linear sequential models over the sequence length
Yi Heng Lim, Qi Zhu, Joshua Selfridge +1
Sequential models, such as Recurrent Neural Networks and Neural Ordinary Differential Equations, have long suffered from slow training due to their inherent sequential nature. For…
cs.LG2020★ 4 cited
-torch: differentiable scientific computing library
Muhammad F. Kasim, Sam M. Vinko
Physics-informed learning has shown to have a better generalization than learning without physical priors. However, training physics-informed deep neural networks requires some asp…
cs.LG2019★ 2 cited
Efficient Parameter Sampling for Neural Network Construction
Drimik Roy Chowdhury, Muhammad Firmansyah Kasim
The customizable nature of deep learning models have allowed them to be successful predictors in various disciplines. These models are often trained with respect to thousands or mi…