Searched for: subject:"Transfer%5C+learning"
(1 - 6 of 6)
document
Duan, Wen Jie (author)
Planning grasp poses for a robot on unknown objects in cluttered environments is still an open problem. Recent research suggests that deep learning technique is a promising approach to plan grasp poses on unknown objects in cluttered environments. In this field, three types of data are used for training: (a) human labeled data; (b) synthetic...
master thesis 2018
document
Rao, Shashank (author)
Sleep is a natural state of our mind and body during which our muscles heal and our memories are consolidated. It is such a habitual phenomenon that we have been viewing it as another ordinary task in our day-to-day life. However, owing to the current fast-paced, technology-driven generation, we are letting ourselves be sleep-deprived, giving...
master thesis 2018
document
Xie, Yu (author)
Facing the severe air pollution phenomenon in urban areas and the subsequent low visibility event in airports, it is urgent to conduct air quality and visibility predictions to better reflect their changing trends. However, the variations of PM2.5 and visibility involve complicated physical and chemical processes, which make their accurate...
master thesis 2018
document
Ju, Jihong (author)
Training data for segmentation tasks are often available only on a small scale. Transferring learned representations from pre-trained classification models is therefore widely adopted by convolutional neural networks for semantic segmentation. In domains where the representations from the classification models are not directly applicable, we...
master thesis 2017
document
Sloots, J.J. (author)
Machine learning approaches are increasingly successful in medical image analysis. Still, learning from MR images poses some serious challenges. Scanner-dependent characteristics effect feature representations directly and hamper the clinical implementation of otherwise successful supervised-learning techniques. To compensate for variations in...
master thesis 2016
document
Langenkamp, W.H. (author)
Reinforcement learning is a machine learning paradigm that deals with optimisation and learns by interacting with its environment. Tabular reinforcement learning methods are popular because of their relative simplicity combined with good guarantees of finding an optimal solution. The downside is that they suffer from an exponentially growing...
master thesis 2016
Searched for: subject:"Transfer%5C+learning"
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