Unsupervised machine learning approach for identifying information threads by leveraging answers to 5W1H questions from documents, the temporal relationships between documents and hierarchical agglomerative clustering (HAC).

**WaveGlow** is a flow-based generative model that generates audio by sampling from a distribution. Specifically samples are taken from a zero mean spherical Gaussian with the same number of dimensions as our desired output, and those samples are put through a series of layers that transforms the simple distribution to one which has the desired distribution.

WaveGlow

WaveGlow: A Flow-based Generative Network for Speech Synthesis

SeqINT

Identifying chronological and coherent information threads using 5W1H questions and temporal relationships

**Mixup** is a data augmentation technique that generates a weighted combination of random image pairs from the training data. Given two images and their ground truth labels: $\left(x\_{i}, y\_{i}\right), \left(x\_{j}, y\_{j}\right)$, a synthetic training example $\left(\hat{x}, \hat{y}\right)$ is generated as:

$$ \hat{x} = \lambda{x\_{i}} + \left(1 − \lambda\right){x\_{j}} $$
$$ \hat{y} = \lambda{y\_{i}} + \left(1 − \lambda\right){y\_{j}} $$

where $\lambda \sim \text{Beta}\left(\alpha = 0.2\right)$ is independently sampled for each augmented example.

Source	Identifying chronological and coherent information threads using 5W1H questions and temporal relationships
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

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What is: Sequential Information Threading?

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