Pedro Lara-Benítez

Pedro Lara-Benítez
Universidad de Sevilla | US · Languages and Systems

Master of Engineering

About

13
Publications
13,883
Reads
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244
Citations
Introduction
PhD Student in Machine Learning. Mainly focused on Deep Learning for time-series forecasting, computer vision and data streams.
Additional affiliations
October 2019 - present
Universidad de Sevilla
Position
  • PhD Student
Description
  • Researching on Machine Learning, Artificial Intelligence and Data science
October 2018 - October 2019
Universidad de Sevilla
Position
  • Research Assistant
Education
October 2018 - July 2019
Universidad de Sevilla
Field of study
  • Software Engineering: Cloud, Data science and IT service management
October 2017 - September 2018
Middlesex University, UK
Field of study
  • Computer Science (Erasmus +)
October 2014 - September 2018
Universidad de Sevilla
Field of study
  • Computer Science - Software Engineering

Publications

Publications (13)
Article
Full-text available
Data streaming classification has become an essential task in many fields where real-time decisions have to be made based on incoming information. Neural networks are a particularly suitable technique for the streaming scenario due to their incremental learning nature. However, the high computation cost of deep architectures limits their applicabil...
Article
Full-text available
Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these types of time series forecasting problem...
Article
Full-text available
In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the t...
Article
Full-text available
Object detection has been one of the most active topics in computer vision for the past years. Recent works have mainly focused on pushing the state-of-the-art in the general-purpose COCO benchmark. However, the use of such detection frameworks in specific applications such as autonomous driving is yet an area to be addressed. This study presents a...
Chapter
Full-text available
The attention-based Transformer architecture is earning increasing popularity for many machine learning tasks. In this study, we aim to explore the suitability of Transformers for time series forecasting, which is a crucial problem in different domains. We perform an extensive experimental study of the Transformer with different architecture and hy...
Article
Full-text available
Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, much...
Chapter
Solar energy is currently among the most important and convenient renewable sources, with a great potential to reduce the use of fossil fuels. However, power generation from solar panels is very irregular and highly dependent on weather conditions. Therefore, solar irradiance forecasting is a fundamental task to ensure an efficient power management...
Preprint
Full-text available
Object detection has been one of the most active topics in computer vision for the past years. Recent works have mainly focused on pushing the state-of-the-art in the general-purpose COCO benchmark. However, the use of such detection frameworks in specific applications such as autonomous driving is yet an area to be addressed. This study presents a...
Preprint
Full-text available
In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the t...
Chapter
Full-text available
Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, ther...
Article
Full-text available
Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance on their suitability when using them in a particular scenario such as autonomous driving. In this work, we aim to assess the perform...
Preprint
Full-text available
Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, ther...
Preprint
Full-text available
Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these type of time series forecasting problems...

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