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This work presents the application of a probabilistic approach to an already existing deep learning model for weather and climate prediction. Probabilistic deep learning allows to capture and address the uncertainties related to the data given as input and ...
We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...
Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce a model for rhythms based on the distributions of di ...
Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique decision ...
This paper is a study on the use of Machine Learning and Deep Learning to determine the similarity between different musics. The first part introduces the framework used and some concepts of Deep Learning that will be used in the algorithms. The second par ...
Music is hierarchically structured, both in how it is perceived by listeners and how it is composed. Such structure can be elegantly captured using probabilistic grammatical models similar to those used to study natural language. They address the complexit ...
Background. Interfacing an amputee's upper-extremity stump nerves to control a robotic hand requires training of the individual and algorithms to process interactions between cortical and peripheral signals. Objective. To evaluate for the first time whethe ...
Humans are able to learn and compose complex, yet beautiful, pieces of music as seen in e.g. the highly complicated works of J.S. Bach. However, how our brain is able to store and produce these very long temporal sequences is still an open question. Long s ...
Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce generative models for melodies. We decompose melodic ...
Recent philosophical, psychological and neuroscientific theories suggest that bodily experience is crucial for the phenomenology of selfhood (i.e. the experience of 'being someone'). In this thesis I have shown that two essential aspects of bodily self-con ...
