In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[EX Q]:    Metric embeddings are   immensely useful representations of associations between entities   (images, users, search queries, words, and more).   Embeddings are learned by  optimizing a loss objective of the general form of a sum over example associations. Typically, the optimization uses stochastic gradient updates over minibatches of examples that are arranged  independently at random. In this work, we propose the use of {\em structured arrangements} through randomized {\em microbatches} of examples that are more likely to include similar ones. We make a principled argument for the properties of our arrangements  that accelerate the training and present efficient algorithms to generate microbatches that respect the marginal  distribution of training examples.   Finally, we observe experimentally that our structured arrangements accelerate training by 3-20\%. Structured arrangements emerge as a powerful and novel performance knob for SGD that is independent and complementary to other SGD  hyperparameters and thus is a candidate for wide deployment.
[EX A]: Accelerating SGD by arranging examples differently

[EX Q]: The reproducibility of reinforcement-learning research has been highlighted as a key challenge area in the field. In this paper, we present a case study in reproducing the results of one groundbreaking algorithm, AlphaZero, a reinforcement learning system that learns how to play Go at a superhuman level given only the rules of the game. We describe Minigo, a reproduction of the AlphaZero system using publicly available Google Cloud Platform infrastructure and Google Cloud TPUs. The Minigo system includes both the central reinforcement learning loop as well as auxiliary monitoring and evaluation infrastructure. With ten days of training from scratch on 800 Cloud TPUs, Minigo can play evenly against LeelaZero and ELF OpenGo, two of the strongest publicly available Go AIs. We discuss the difficulties of scaling a reinforcement learning system and the monitoring systems required to understand the complex interplay of hyperparameter configurations.
[EX A]: We reproduced AlphaZero on Google Cloud Platform

[EX Q]: Sequence-to-sequence attention-based models are a promising approach for end-to-end speech recognition. The increased model power makes the training procedure more difficult, and analyzing failure modes of these models becomes harder because of the end-to-end nature. In this work, we present various analyses to better understand training and model properties. We investigate on pretraining variants such as growing in depth and width, and their impact on the final performance, which leads to over 8% relative improvement in word error rate. For a better understanding of how the attention process works, we study the encoder output and the attention energies and weights. Our experiments were performed on Switchboard, LibriSpeech and Wall Street Journal.
[EX A]:
improved pretraining, and analysing encoder output and attention