Combining Language and Vision with a Multimodal Skip-gram Model

We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM) build vector-based word representations by learning to predict linguistic contexts in text corpora. However, for a restricted set of words, the models are also exposed to visual representations of the objects they denote (extracted from natural images), and must predict linguistic and visual features jointly. The MMSKIP-GRAM models achieve good performance on a variety of semantic benchmarks. Moreover, since they propagate visual information to all words, we use them to improve image labeling and retrieval in the zero-shot setup, where the test concepts are never seen during model training. Finally, the MMSKIP-GRAM models discover intriguing visual properties of abstract words, paving the way to realistic implementations of embodied theories of meaning.Comment: accepted at NAACL 2015, camera ready version, 11 page

APPROACHES AND CHARACTERISTICS OF LEARNING DIFFICULTIES IN HIGH SCHOOL STUDENTS

Learning disabilities are the most numerous group of special educational needs and refer to people who do not benefit from formal education and present in the majority of their difficulties in reading and decoding. In the work, you attempt to theoretically understand the information in relation to the physiology of learning difficulties, so that teachers are able to understand and subsequently intervene by improving the cognitive function of those individuals who present a strong differentiation of learning image compared to other children, which have low school performance. Early detection of learning difficulties allows for early and effective intervention. The thesis is structured in bibliographic research references and its writing was also based on my many years of teaching experience.  Article visualizations