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]: In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops. These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation. However, transliterating between older and newer texts is a laborious task when performed manually. We introduce two related methods to help solve this transliteration problem automatically, given that there were not enough data to train an end-to-end deep learning model. One approach is implemented, end-to-end, using finite state transducers (FSTs). The other is a hybrid deep learning approach which approximately composes an FST with a recurrent neural network (RNN). We find that the hybrid approach outperforms the end-to-end FST by partitioning the original problem into one part that can be modelled by hand, using an FST, and into another part, which is easily solved by an RNN trained on the available data.
[EX A]: A novel, hybrid deep learning approach provides the best solution to a limited-data problem (which is important to the conservation of the Hawaiian language)

[EX Q]: The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists. Two major reasons are that neurons would need to send two different types of signal in the forward and backward phases, and that pairs of neurons would need to communicate through symmetric bidirectional connections.
 We present a simple two-phase learning procedure for fixed point recurrent networks that addresses both these issues.
 In our model, neurons perform leaky integration and synaptic weights are updated through a local mechanism.
 Our learning method extends the framework of Equilibrium Propagation to general dynamics, relaxing the requirement of an energy function.
 As a consequence of this generalization, the algorithm does not compute the true gradient of the objective function,
 but rather approximates it at a precision which is proven to be directly related to the degree of symmetry of the feedforward and feedback weights.
 We show experimentally that the intrinsic properties of the system lead to alignment of the feedforward and feedback weights, and that our algorithm optimizes the objective function.
[EX A]:
We describe a biologically plausible learning algorithm for fixed point recurrent networks without tied weights