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Languages follow many statistical regularities called laws. Perhaps the most popular example is Zipf's law for word frequencies, that relates the frequency of a word with its rank, but other laws have been formulated, such as the law of abbreviation, the law of meaning-distribution, the meaning-frequency law,...and so on (Zipf 1949). About 15 years ago, a family of optimization models was introduced to shed light on the origins of Zipf's law for word frequencies (Ferrer-i-Cancho & Solé 2003, Ferrer-i-Cancho 2005). In that family, language is modelled as a bipartite graph where words connect to meanings and a cost function is defined based on the structure of that graph. A simple Monte Carlo algorithm was used to minimize the cost function while the structure of the graph was allowed to vary. Recently, it has been shown how these models shed light on how children learn words (Ferrer-i-Cancho 2017). The aim of this project is to investigate new versions of these models (e.g., Ferrer-i-Cancho & Vitevitch 2018) in two directions: (1) Providing an efficient implementation of the optimization algorithm. (2) Comparing the statistical properties of the model against the statistical properties of natural communication systems.

The master thesis consists of developing a framework for Group Recommender Systems and investigating the methods for generating recommendations to groups.

Boltzmann Machines are probabilistic models developed in 1985 by D.H. Ackley, G.E. Hinton and T.J. Sejnowski. In 2006, Restricted Boltzmann Machines (RBMs) were used in the pre-training step of several successful deep learning models, leading to a new renaissance of neural networks and artificial intelligence. In spite of their nice mathematical formulation, there are a number of issues that are hard to compute: - The computation of the partition function is NP-hard, involving an exponential sum of terms - The exact computation of the derivative of the log-likelihood is also NP-hard, since it contains the derivative of the partition function Therefore, in practice we have to approximate both the computation of the probabilities and several components of the learning process itself. These drawbacks have prevented RBMs to show their real potential as truly probabilistic models. Currently, we are working on trying to improve several of the unsolved issues related to RBMs: - Mechanisms to control the learning process www.lsi.upc.edu/%7Eeromero/Publications/Downloads/2018-tnnls-stopcritRBM.pdf - Better approximations of the derivative of the log-likelihood http://www.lsi.upc.edu/%7Eeromero/Publications/Downloads/2019-nn-weightedCD.pdf - Efficient approximation of the partition function (work in progress) These works have opened new lines of research, some of which can be the topic of a Master's Thesis. The scope and degree of depth of the work can be adapted to the estimated times to complete the Thesis. For further details, contact Enrique Romero ( ).

We propose to a student or multiple students to work on processing techniques using Deep Learning (Convolutional Neural networks, Generative Adversarial Networks, Semantic Segmentation Networks) to detect and classify marine mammals in photographs and satellite imagery. The computational capacity offered by these new tools will allow the scientific community to better study endangered species and to give an adequate and rapid response to face the current biodiversity crisis.

Ingeniería del Conocimiento y Aprendizaje Automático Modelado, Razonamiento y Solución de Problemas

The goal of this project is to analyze state-of-the-art metrics[1] that combines them to quantify the equality of learning opportunities among learners and mitigate the inequalities generated by the recommender systems as post-processed approach that balances personalization and learning opportunity equality in recommendations

The goal of this project is to analyze and mitigate bias in collaborative filtering recommender systems.

Consulta ofertas de otros estudios y especialidades