Apprentissage d'espaces sémantiques
In this work, we focus on learning semantic spaces for multiple domains, but also at the intersection of different domains. The semantic space is where the learned representation lives. This space is called semantic if similar entities from a human perspective have their similarity preserved in this space. We use different machine learning algorithms to learn representations with interesting intrinsic properties. The first article presents a pipeline including many different unsupervised learning techniques used to win the Unsupervised and Transfer Learning Challenge in 2011. In the second article, we present a pipeline taking advantage of the structure of the data for a scene classification problem. This approach allows us to drastically reduce the dimensionality while improving significantly on the scene recognition accuracy. The third article focuses on the space structure learned by deep representations. We show that performing the sampling procedure from deeper levels of representation space explores more of the different classes. In the fourth article, we tackle a semantic parsing problem with several Recurrent Neural Network architectures taking as input context windows of word embeddings. In the fifth article, an investigation on learning a single semantic space at the intersection of words and images is presented. We propose a way to perform ”augmented search” where a search on an image containing an object would also return images of the object’s parts.
Semantics Deep learning Machine learning Recurrent neural networks Neural networks Natural language processing Computer vision