Automated functional outcome prediction in stroke using combined imaging and clinical parameters

Predicting functional outcome after intra-arterial treatment (IAT) in acute ischemic stroke (AIS) patients is an important aspect of treatment decision making and prognostics. Standard methods for functional outcome prediction after stroke combine baseline clinical (and radiological) parameters. In this study, we investigated to what extent...

Deep Neural Networks and Optimization: A promising tag team

Machine learning has been a computer sciences buzzword for years. The technology has a lot of potential and a huge number of applications that spoke to people with and without knowledge of computer sciences. Image, text and speech recognition, social profiling, computergames, everything seemed possible. Machine learning is not as much in the...

LMI-based Stability Analysis for Learning Control: Deep Neural Networks and Locally Weighted Learning

Learning capabilities are a key requisite for an autonomous agent operating in dynamically changing and complex environments, where pre-programming is not anymore possible. Furthermore, it is essential to guarantee that the learning agent will act safely by considering its stability properties. In this thesis, novel conditions are proposed,...

Reinforcement Learning Across Timescales

This project addresses a fundamental problem faced by many reinforcement learning agents. Commonly used reinforcement learning agents can be seen to have deteriorating performances at increasing frequencies, as they are unable to correctly learn the ordering of expected returns for actions that are applied. We call this the disappearing...

Deep Reinforcement Learning for Bipedal Robots

Reinforcement Learning (RL) is a general purpose framework for designing controllers for non-linear systems. It tries to learn a controller (policy) by trial and error. This makes it highly suitable for systems which are difficult to control using conventional control methodologies, such as walking robots. Traditionally, RL has only been...

Limits on Modeling Compensation in Multimodal DNNs for Audio Visual Speech Recognition

Speech is a natural way of communicating that does not require us to develop any new skills in order to be able to interact with electronic devices. With the evolution of technology, speech has become one of the primary means of communication. Speech recognition is a form of multimedia content analysis, where the information carried in a speech...