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.

Input: Consider Input: 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.

Output: We release a dataset constructed from single-lead ECG data from 11,000 patients who were prescribed to use the {DEVICENAME}(TM) device.

Input: Consider Input: Neural networks with low-precision weights and activations offer compelling
efficiency advantages over their full-precision equivalents. The two most
frequently discussed benefits of quantization are reduced memory consumption,
and a faster forward pass when implemented with efficient bitwise
operations. We propose a third benefit of very low-precision neural networks:
improved robustness against some adversarial attacks, and in the worst case,
performance that is on par with full-precision models. We focus on the very
low-precision case where weights and activations are both quantized to $\pm$1,
and note that stochastically quantizing weights in just one layer can sharply
reduce the impact of iterative attacks. We observe that non-scaled binary neural
networks exhibit a similar effect to the original \emph{defensive distillation}
procedure that led to \emph{gradient masking}, and a false notion of security.
We address this by conducting both black-box and white-box experiments with
binary models that do not artificially mask gradients.

Output: We conduct adversarial attacks against binarized neural networks and show that we reduce the impact of the strongest attacks, while maintaining comparable accuracy in a black-box setting

Input: Consider Input: Recurrent Neural Networks (RNNs) are widely used models for sequence data. Just like for feedforward networks, it has become common to build "deep" RNNs, i.e., stack multiple recurrent layers to obtain higher-level abstractions of the data. However, this works only for a handful of layers. Unlike feedforward networks, stacking more than a few recurrent units (e.g., LSTM cells) usually hurts model performance, the reason being vanishing or exploding gradients during training. We investigate the training of multi-layer RNNs and examine the magnitude of the gradients as they propagate through the network. We show that, depending on the structure of the basic recurrent unit, the gradients are systematically attenuated or amplified, so that with an increasing depth they tend to vanish, respectively explode. Based on our analysis we design a new type of gated cell that better preserves gradient magnitude, and therefore makes it possible to train deeper RNNs. We experimentally validate our design with five different sequence modelling tasks on three different datasets. The proposed stackable recurrent (STAR) cell allows for substantially deeper recurrent architectures, with improved performance.
Output: We analyze the gradient propagation in deep RNNs and from our analysis, we propose a new multi-layer deep RNN.