Semi-supervised learning of deep hierarchical hidden representations

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Until the end of the 20th century, most of the computer programs were manuallyimplemented to perform repetitive tasks that could be automated, thus alleviating humanwork. However, at the end of the century, the field of Machine Learning emerged in order tocreate algorithms that could generate programs automatically by means of data andexamples. These methods together with an exponential growth of available data andcomputational power allowed the training deep hierarchical models. Nowadays, deephierarchical models are achieving and occasionally surpassing human performance on avariety 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 fullyannotated data to solve any specific task. This type of problems is known as SupervisedLearning tasks. For this reason, one of the bottlenecks for training these models is thegeneration 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 beenannotated in order to help the Supervised Learning part. For example, in problems wherelabels are scarce, it is possible to use unlabeled data to learn hierarchical hiddenrepresentations that can be used to improve the performance of Supervised models. Newmethods are still being investigated and this is one of the main topics of this Ph.D. Anotherproblem is that most of the current literature in Machine Learning assumes that dataavailable during the training of the models follows the same distribution as the future dataavailable during the deployment time. However, this assumption is only true in a fewcontrolled scenarios; for example in a closed factory. On the contrary, most of the real casescenarios evolve and change with new objects, words or patterns. For this reason, it isimportant to provide Machine Learning models with the ability to notify when new patternsappear, thus avoiding possible mistakes.This Ph.D. proposes to address this problem by means of Semi-Supervised Learningtechniques. Using new techniques we want to enhance existent models by giving them theability to discern between known and unknown patterns. In such a way that models are ableto manifest the confidence on their predictions. This is important in order to make confidentpredictions 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 deephierarchical hidden representations of the data. These models are being applied in a widerange of problems, some of which have important implications. However, they need largeamounts 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, makethem aware of new situations, and able to avoid uninformed decisions.