Semi-supervised learning of deep hierarchical hidden representations

Until the end of the 20th century, most of the computer programs were manually
implemented to perform repetitive tasks that could be automated, thus alleviating human
work. However, at the end of the century, the field of Machine Learning emerged in order to
create algorithms that could generate programs automatically by means of data and
examples. These methods together with an exponential growth of available data and
computational power allowed the training deep hierarchical models. Nowadays, deep
hierarchical models are achieving and occasionally surpassing human performance on a
variety of tasks like object recognition, automatic translation, speech recognition,
autonomous transportation and medical applications.
One of the main problems of the current state-of-the-art models is that they need fully
annotated data to solve any specific task. This type of problems is known as Supervised
Learning tasks. For this reason, one of the bottlenecks for training these models is the
generation of good and large datasets, as they require lots of manual annotation.
To solve this problem, the field of Semi-Supervised learning uses data that has not been
annotated in order to help the Supervised Learning part. For example, in problems where
labels are scarce, it is possible to use unlabeled data to learn hierarchical hidden
representations that can be used to improve the performance of Supervised models. New
methods are still being investigated and this is one of the main topics of this Ph.D. Another
problem is that most of the current literature in Machine Learning assumes that data
available during the training of the models follows the same distribution as the future data
available during the deployment time. However, this assumption is only true in a few
controlled scenarios; for example in a closed factory. On the contrary, most of the real case
scenarios evolve and change with new objects, words or patterns. For this reason, it is
important to provide Machine Learning models with the ability to notify when new patterns
appear, thus avoiding possible mistakes.
This Ph.D. proposes to address this problem by means of Semi-Supervised Learning
techniques. Using new techniques we want to enhance existent models by giving them the
ability to discern between known and unknown patterns. In such a way that models are able
to manifest the confidence on their predictions. This is important in order to make confident
predictions given familiar patterns while being able to ask for further inspection otherwise.
In conclusion, there is an increasing popularity of machine learning models that learn deep
hierarchical hidden representations of the data. These models are being applied in a wide
range of problems, some of which have important implications. However, they need large
amounts of annotated data and are not able to output confidence values in their predictions.
For that reason, the topic of this Ph.D. is to improve models using unlabeled data, make
them aware of new situations, and able to avoid uninformed decisions.