Academic Search and Relevance: deep searching what you are looking for

Let's see some more examples of Academic Search and Relevance. This time around from my domain of expertise which is Machine Learning. Again side-by-side comparison and we will show why directly matching the users' needs is important.

{deep learning autoencoders}

Here I am interested in finding a specific innovation discovered in deep learning. As discussed in a previous post autoencoders are deep learning machines which are able to auto-learn what are the important features in a dataset with no human intervention. The machine will pick the right features on your behalf with no handcraft work.

Google returns the seminal paper  from 2006 which is considered the starting point for the renaissance of Neural Networks and their evolution into modern Deep Learning systems.

However, this paper DOES NOT talk about Autoencoders, Instead, it talks about deep believe nets a slightly related topic. At the time of that paper Autoencoders where NOT YET popular for Deep Learning (and even Deep Learning was not invented as a new word yet).

Therefore, I'd consider this a DSAT because it is not immediately satisfying my very specific search needs.

So Google Scholar is not returning a very relevant result

ScienceDirect is instead returning a very relevant and recent results discussing about Deep Learning and Autoencoders.