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Natural language processing techniques are dependent upon punctuation to work well. When their input is taken from speech recognition, it is necessary to reconstruct the punctuation; in particular sentence boundaries. We define a range of features from low ...
Optical Character Recognition (OCR) is an extensive research field in image processing and pattern recognition. Traditional character recognition methods cannot distinguish a character or a word from a scanned image. This paper proposes a system, which is ...
Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condense ...
This master thesis explores ways to apply geometric deep learning to the field of numerical simulations with an emphasis on the Navier-Stokes equations. With the recent success of Deep Learning, there should be room for experimentation also in the field of ...
In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these ...
Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condense ...
Natural language processing techniques are dependent upon punctuation to work well. When their input is taken from speech recognition, it is necessary to reconstruct the punctuation; in particular sentence boundaries. We define a range of features from low ...
One of the paramount challenges in neuroscience is to understand the dynamics of individual neurons and how they give rise to network dynamics when interconnected. Historically, researchers have resorted to graph theory, statistics, and statistical mechani ...
Humans have a remarkable way of learning, adapting and mastering new manipulation
tasks. With the current advances in Machine Learning (ML), the promise of having
robots with such capabilities seems to be on the cusp of reality. Transferring human-level
sk ...
The Koopman operator was recently shown to be a useful method for nonlinear system identification and controller design. However, the scalability of current data-driven approaches is limited by the selection of feature maps. In this paper, we present a new ...