  Mini-batch gradient descent based methods are the de facto algorithms for
training neural network architectures today. We introduce a mini-batch
selection strategy based on submodular function maximization. Our novel
submodular formulation captures the informativeness of each sample and
diversity of the whole subset. We design an efficient, greedy algorithm which
can give high-quality solutions to this NP-hard combinatorial optimization
problem. Our extensive experiments on standard datasets show that the deep
models trained using the proposed batch selection strategy provide better
generalization than Stochastic Gradient Descent as well as a popular baseline
sampling strategy across different learning rates, batch sizes, and distance
metrics.
