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pro vyhledávání: '"Fawaz, Hassan Ismail"'
Autor:
Fawaz, Hassan Ismail, Del Grosso, Ganesh, Kerdoncuff, Tanguy, Boisbunon, Aurelie, Saffar, Illyyne
Unsupervised Domain Adaptation (UDA) aims to harness labeled source data to train models for unlabeled target data. Despite extensive research in domains like computer vision and natural language processing, UDA remains underexplored for time series
Externí odkaz:
http://arxiv.org/abs/2312.09857
Autor:
Ismail-Fawaz, Ali, Fawaz, Hassan Ismail, Petitjean, François, Devanne, Maxime, Weber, Jonathan, Berretti, Stefano, Webb, Geoffrey I., Forestier, Germain
Time series data can be found in almost every domain, ranging from the medical field to manufacturing and wireless communication. Generating realistic and useful exemplars and prototypes is a fundamental data analysis task. In this paper, we investig
Externí odkaz:
http://arxiv.org/abs/2309.16353
Autor:
Fawaz, Hassan Ismail
Time series analysis is a field of data science which is interested in analyzing sequences of numerical values ordered in time. Time series are particularly interesting because they allow us to visualize and understand the evolution of a process over
Externí odkaz:
http://arxiv.org/abs/2010.00567
Autor:
Fawaz, Hassan Ismail, Lucas, Benjamin, Forestier, Germain, Pelletier, Charlotte, Schmidt, Daniel F., Weber, Jonathan, Webb, Geoffrey I., Idoumghar, Lhassane, Muller, Pierre-Alain, Petitjean, François
This paper brings deep learning at the forefront of research into Time Series Classification (TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of time series. The last few decades of work in this area have led t
Externí odkaz:
http://arxiv.org/abs/1909.04939
Autor:
Fawaz, Hassan Ismail, Forestier, Germain, Weber, Jonathan, Idoumghar, Lhassane, Muller, Pierre-Alain
Purpose: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unpreceden
Externí odkaz:
http://arxiv.org/abs/1908.07319
Autor:
Fawaz, Hassan Ismail, Forestier, Germain, Weber, Jonathan, Petitjean, François, Idoumghar, Lhassane, Muller, Pierre-Alain
Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many other types of data du
Externí odkaz:
http://arxiv.org/abs/1904.07302
Autor:
Fawaz, Hassan Ismail, Forestier, Germain, Weber, Jonathan, Idoumghar, Lhassane, Muller, Pierre-Alain
Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety. With the recent success of deep neural networks in various domains such as
Externí odkaz:
http://arxiv.org/abs/1903.07054
Autor:
Fawaz, Hassan Ismail, Forestier, Germain, Weber, Jonathan, Idoumghar, Lhassane, Muller, Pierre-Alain
Deep neural networks have revolutionized many fields such as computer vision and natural language processing. Inspired by this recent success, deep learning started to show promising results for Time Series Classification (TSC). However, neural netwo
Externí odkaz:
http://arxiv.org/abs/1903.06602
Autor:
Fawaz, Hassan Ismail, Forestier, Germain, Weber, Jonathan, Idoumghar, Lhassane, Muller, Pierre-Alain
Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset. This idea has be
Externí odkaz:
http://arxiv.org/abs/1811.01533
Autor:
Fawaz, Hassan Ismail, Forestier, Germain, Weber, Jonathan, Idoumghar, Lhassane, Muller, Pierre-Alain
Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Ne
Externí odkaz:
http://arxiv.org/abs/1809.04356