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Balanced Group Convolution: An Improved Group Convolution Based on Approximability Estimates
The performance of neural networks has been significantly improved by increasing the number of channels in convolutional layers.
Two-level Group Convolution
In this paper, we propose a new convolution methodology called ``two-level'' group convolution that is robust with respect to the increase of the number of groups and suitable for multi-GPU parallel computation.
Parareal Neural Networks Emulating a Parallel-in-time Algorithm
We observe that layers of DNN can be interpreted as the time step of a time-dependent problem and can be parallelized by emulating a parallel-in-time algorithm called parareal.
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