Minibatch stochastic gradient descent offers the best of both worlds: computational and statistical efficiency. In minibatch stochastic gradient descent we process batches of data obtained by a random ...

At the heart of the decision to use minibatches is computational efficiency. This is most easily understood when considering parallelization to multiple GPUs and multiple servers. In this case we need ...

Abstract: Machine learning, especially deep neural networks, has developed rapidly in fields, including computer vision, speech recognition, and reinforcement learning. Although minibatch stochastic ...

Abstract: Machine learning, especially deep neural networks, has developed rapidly in fields, including computer vision, speech recognition, and reinforcement learning. Although minibatch stochastic ...