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.

Q: The linear transformations in converged deep networks show fast eigenvalue decay. The distribution of eigenvalues looks like a Heavy-tail distribution, where the vast majority of eigenvalues is small, but not actually zero, and only a few spikes of large eigenvalues exist.
 We use a stochastic approximator to generate histograms of eigenvalues. This allows us to investigate layers with hundreds of thousands of dimensions. We show how the distributions change over the course of image net training, converging to a similar heavy-tail spectrum across all intermediate layers.

A: We investigate the eigenvalues of the linear layers in deep networks and show that the distributions develop heavy-tail behavior during training.
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Q: In recent years we have made significant progress identifying computational principles that underlie neural function. While not yet complete, we have sufficient evidence that a synthesis of these ideas could result in an understanding of how neural computation emerges from a combination of innate dynamics and plasticity, and which could potentially be used to construct new AI technologies with unique capabilities. I discuss the relevant principles, the advantages they have for computation, and how they can benefit AI. Limitations of current AI are generally recognized, but fewer people are aware that we understand enough about the brain to immediately offer novel AI formulations.


A: Limitations of current AI are generally recognized, but fewer people are aware that we understand enough about the brain to immediately offer novel AI formulations.
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Q: Using modern deep learning models to make predictions on time series data from wearable sensors generally requires large amounts of labeled data. However, labeling these large datasets can be both cumbersome and costly. In this paper, we apply weak supervision to time series data, and programmatically label a dataset from sensors worn by patients with Parkinson's. We then built a LSTM model that predicts when these patients exhibit clinically relevant freezing behavior (inability to make effective forward stepping). We show that (1) when our model is trained using patient-specific data (prior sensor sessions), we come within 9% AUROC of a model trained using hand-labeled data and (2) when we assume no prior observations of subjects, our weakly supervised model matched performance with hand-labeled data. These results demonstrate that weak supervision may help reduce the need to painstakingly hand label time series training data.

A:
We demonstrate the feasibility of a weakly supervised time series classification approach for wearable sensor data. 
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