Pig was originally developed at Yahoo Research around 2006 for researchers to have an ad-hoc way of creating and executing map-reduce jobs on very large data sets. In 2007, it was moved into the Apache Software Foundation.
SParse Interpretable Neural Embeddings (SPINE)
Prediction without justification has limited utility. Much of the success of neural models can be attributed to their ability to learn rich, dense and expressive representations. While these representations capture the underlying complexity and latent trends in the data, they are far from being interpretable. We propose a novel variant of denoising k-sparse autoencoders that generates highly efficient and interpretable distributed word representations (word embeddings), beginning with existing word representations from state-of-the-art methods like GloVe and word2vec. Through large scale human evaluation, we report that our resulting word embedddings are much more interpretable than the original GloVe and word2vec embeddings. Moreover, our embeddings outperform existing popular word embeddings on a diverse suite of benchmark downstream tasks. …
Attribution modelling, in essence, means reporting on the impact of communication activity using metrics like:
• Customer retention
• Volume of sales
Instead of metrics like:
• Share of voice
• Web visits
• Click through rate (CTR)
There’s a big difference between these two lists. The second list contains important metrics, but businesses could survive without ever increasing them. The business metrics in the first list, however, are essential for all companies that want to survive and thrive.
Understanding the impact of communications on business metrics is – rightly – more important to senior executives. This is the primary objective of attribution modelling; to provide holistic, accurate information about the financial return activities are delivering so you can refine them, adjust what you’re doing, and use the same budget to deliver more value to your business and your customers. …