Tesis - Maestría en Ciencias de la Computación

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https://hdl.handle.net/20.500.12590/18032

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Browsing Tesis - Maestría en Ciencias de la Computación by browse.metadata.advisor "Ochoa Luna, Jose Eduardo"

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    An adversarial model for paraphrase generation
    (Universidad Católica San Pablo, 2020) Vizcarra Aguilar, Gerson Waldyr; Ochoa Luna, Jose Eduardo
    Paraphrasing is the action of expressing the idea of a sentence using different words. Paraphrase generation is an interesting and challenging task due mainly to three reasons: (1) The nature of the text is discrete, (2) it is difficult to modify a sentence slightly without changing the meaning, and (3) there are no accurate automatic metrics to evaluate the quality of a paraphrase. This problem has been addressed with several methods. Even so, neural network-based approaches have been tackling this task recently. This thesis presents a novel framework to solve the paraphrase generation problem in English. To do so, this work focuses and evaluates three aspects of a model, as the teaser figure shows. (a) Static input representations extracted from pre-trained language models. (b) Convolutional sequence to sequence models as our main architecture. (c) Hybrid loss function between maximum likelihood and adversarial REINFORCE, avoiding the computationally expensive Monte-Carlo search. We compare our best models with some baselines in the Quora question pairs dataset. The results show that our framework is competitive against the previous benchmarks.
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    Deep learning models for spatial prediction of fine particulate matter
    (Universidad Católica San Pablo, 2023) Colchado Soncco, Luis Ernesto; Ochoa Luna, Jose Eduardo
    Studies indicate that air pollutant concentrations affect human health. Especially, Fine Particulate Matter (PM2.5) is the most dangerous pollutant because this is related to cardiovascular and respiratory diseases, among others. Therefore, governments must monitor and control pollutant concentrations. To this end, many of them have implemented Air quality monitoring (AQM) networks. However, AQM stations are usually spatially sparse due to their high costs in implementation and maintenance, leaving large áreas without a measure of pollution. Numerical models based on the simulation of diffusion and reaction process of air pollutants have been proposed to infer their spatial distribution. However, these models often require an extensive inventory of data and variables, as well as high-end computing hardware. In this research, we propose two deep learning models. The first is a generative model called Conditional Generative adversarial Network (cGAN). Additionally, we add a loss based on the predicted observation and the k nearest neighbor stations to smooth the randomness of adversarial learning. This variation is called Spatial-learning cGAN (cGANSL), which got better performance for spatial prediction. To interpolate PM2.5 on a location, cGANSL and classical methods like Inverse Distance Weighting (IDW) need to select the k nearest neighbor stations based on straight distance. However, this selection may leave out data from more distant neighbors that could provide valuable information. In this sense, the second proposed model in this study is a Neural Network with an attention-based layer. This model uses a recently proposed attention layer to build a structured graph of the AQM stations, where each station is a graph node to weight the k nearest neighbors for nodes based on attention kernels. The learned attention layer can generate a transformed feature representation for unobserved location, which is further processed by a neural network to infer the pollutant concentration. Based on data from AQM network in Beijing, meteorological conditions, and information from satellite products such as vegetation index (NDVI) and human activity or population-based on Nighttime Light producto (NTL). The cGANSL had a better performance than IDW, Ordinary Kriging (OK), and Neural Network with an attention mechanism. In this experiment, spatial prediction models that selected the k nearest neighbors had a good performance. That may be AQM station Beijing’s high correlation between them. However, using data from the AQM network of Sao Paulo, where AQM stations have a low correlation, the Neural network with an attention-based layer have better performance than IDW, OK, and cGANSL. Besides, the normalized attention weights computed by our attention model showed that in some cases, the attention given to the nearest nodes is independent of their spatial distances. Therefore, the attention model is more flexible since it can learn to interpolate PM2.5 concentration levels based on the available data of the AQM network and some context information. Finally, we found that NDVI and NTL are high related to air pollutant concentration predicted by the attention model.