  Machine learning, especially deep neural networks, has been rapidly developed
in fields including computer vision, speech recognition and reinforcement
learning. Although Mini-batch SGD is one of the most popular stochastic
optimization methods in training deep networks, it shows a slow convergence
rate due to the large noise in gradient approximation. In this paper, we
attempt to remedy this problem by building more efficient batch selection
method based on typicality sampling, which reduces the error of gradient
estimation in conventional Minibatch SGD. We analyze the convergence rate of
the resulting typical batch SGD algorithm and compare convergence properties
between Minibatch SGD and the algorithm. Experimental results demonstrate that
our batch selection scheme works well and more complex Minibatch SGD variants
can benefit from the proposed batch selection strategy.
