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. However, this increase in performance comes with a higher computational cost, resulting in numerous studies focused on reducing it. One promising approach to address this issue is group convolution, which effectively reduces the computational cost by grouping channels. However, to the best of our knowledge, there has been no theoretical analysis on how well the group convolution approximates the standard convolution. In this paper, we mathematically analyze the approximation of the group convolution to the standard convolution with respect to the number of groups. Furthermore, we propose a novel variant of the group convolution called balanced group convolution, which shows a higher approximation with a small additional computational cost. We provide experimental results that validate our theoretical findings and demonstrate the superior performance of the balanced group convolution over other variants of group convolution.Comment: 26pages, 2 figure

Parareal Neural Networks Emulating a Parallel-in-time Algorithm

As deep neural networks (DNNs) become deeper, the training time increases. In this perspective, multi-GPU parallel computing has become a key tool in accelerating the training of DNNs. In this paper, we introduce a novel methodology to construct a parallel neural network that can utilize multiple GPUs simultaneously from a given DNN. 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. The parareal algorithm consists of fine structures which can be implemented in parallel and a coarse structure which gives suitable approximations to the fine structures. By emulating it, the layers of DNN are torn to form a parallel structure which is connected using a suitable coarse network. We report accelerated and accuracy-preserved results of the proposed methodology applied to VGG-16 and ResNet-1001 on several datasets

Which Tasks Will Technology Take? A New Systematic Methodology to Measure Task Automation

With the rapid advance of digital technologies, task automation has recently come to the forefront of the debate on skill-biased technological change. Building on a network theory, this study develops a new systematic methodology to identify comprehensive task types in the overall economy, and to quantitatively measure the degree of automation for each task type. Using comprehensive dataset on occupational skill requirements in 2015, we construct a two-mode network, and identify 13 task types using a non-parametric clustering algorithm. Our findings suggest that routine cognitive task and information processing are most automated tasks, and that flexible thinking and dynamic physical task are least susceptible to automation in 2015. The major contribution of our approach lies in the estimation of degree of automation for different task types. The methodology presents a promising avenue for evaluating the impact of automation on labor market outcomes, such as wage inequality and job polarization

A New Method for Quantitative Evaluation of Neurological Disorders based on EMG signals

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