Thesis offers

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 ( ).

This project is a continuation of two previous master thesis developed in the second terms of courses 2017-2018 and 2018-2019. In the framework of the Tokyo2020 Olympic Games Weather Project, leaded by TriM s.r.l. company and funded by Austrian Sailing Federation, Croatia and Cyprus Laser Olympic classes, a big amount of data have been and are currently collected on the sea through real time sensors during trainings and racings and are being stored into a cloud database. The collaboration with UPC is aimed at developing a data analysis methodology able to support sailors decisions during Olympic Games races. Sailing strategy and performance are strongly related with environmental parameters such as weather, oceanic current and geographical data. A thorough prediction of the conditions expected during a sailing race is a valuable information for a sailor, as it completely conditions his/her tactics during the race. With the aim of developing a decision support system valid for the Olympic Classes Sailing Venues, the following components will be developed and integrated together into one single web-based platform: 1. Wind component 2. Waves component 3. Oceanic current component 4. Boat Performance component. The present master thesis proposal is related with the wind component. The goal is to develop a methodology/procedure to analyse the 'recorded wind dataset' and to recognize significant wind patterns, in other words characteristic features of the wind speed and direction related with the other weather parameters of the day (air pressure, air and water temperature, etc..) and with the geographical position of the specific racing area. A similar analysis should be performed on the 'weather prediction model dataset' and for instance on the wind parameter of the model, to find correlations between predicted and measured values. This step is fundamental for the validation of the weather model that will be used daily during the Olympic Games.

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.

In recent years the volume of information available electronically has increased exponentially, coining the term Big Data to refer to this phenomenon. The medical domain is an area in which the number of documents generated by the centers for patient primary care constantly increases. However, a bottleneck is generated because processing these documents requires specialized personnel craftly performing tasks. In the framework of GraphMed research project, we are developing a set of processors that allow automatic analysis of medical texts taking into account criteria of robustness, high precision and coverage. In particular, this thesis would aim at the extraction of semantic graphs related to medical records and acquisition of patterns of clinical behavior.