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]: We introduce MultiGrain, a neural network architecture that generates compact image embedding vectors that solve multiple tasks of different granularity: class, instance, and copy recognition. MultiGrain is trained jointly for classification by optimizing the cross-entropy loss and for instance/copy recognition by optimizing a self-supervised ranking loss. The self-supervised loss only uses data augmentation and thus does not require additional labels. Remarkably, the unified embeddings are not only much more compact than using several specialized embeddings, but they also have the same or better accuracy. When fed to a linear classifier, MultiGrain using ResNet-50 achieves 79.4% top-1 accuracy on ImageNet, a +1.8% absolute improvement over the the current state-of-the-art AutoAugment method. The same embeddings perform on par with state-of-the-art instance retrieval with images of moderate resolution. An ablation study shows that our approach benefits from the self-supervision, the pooling method and the mini-batches with repeated augmentations of the same image.

[EX A]: Combining classification and image retrieval in a neural network architecture, we obtain an improvement for both tasks.

[EX Q]: We release the largest public ECG dataset of continuous raw signals for representation learning containing over 11k patients and 2 billion labelled beats. Our goal is to enable semi-supervised ECG models to be made as well as to discover unknown subtypes of arrhythmia and anomalous ECG signal events. To this end, we propose an unsupervised representation learning task, evaluated in a semi-supervised fashion.   We provide a set of baselines for different feature extractors that can be built upon.   Additionally, we perform qualitative evaluations on results from PCA embeddings, where we identify some clustering of known subtypes indicating the potential for representation learning in arrhythmia sub-type discovery.
[EX A]: We release a dataset constructed from single-lead ECG data from 11,000 patients who were prescribed to use the {DEVICENAME}(TM) device.

[EX Q]: The lack of crisp mathematical models that capture the structure of real-world
 data sets is a major obstacle to the detailed theoretical understanding of deep
 neural networks. Here, we first demonstrate the effect of structured data sets
 by experimentally comparing the dynamics and the performance of two-layer
 networks trained on two different data sets: (i) an unstructured synthetic data
set containing random i.i.d. inputs, and (ii) a simple canonical data set such
 as MNIST images. Our analysis reveals two phenomena related to the dynamics of
 the networks and their ability to generalise that only appear when training on
 structured data sets. Second, we introduce a generative model for data sets,
 where high-dimensional inputs lie on a lower-dimensional manifold and have
 labels that depend only on their position within this manifold. We call it the
 *hidden manifold model* and we experimentally demonstrate that training
 networks on data sets drawn from this model reproduces both the phenomena seen
 during training on MNIST.
[EX A]:
We demonstrate how structure in data sets impacts neural networks and introduce a generative model for synthetic data sets that reproduces this impact.