Thèse de doctorat : Università della Svizzera italiana, 2020 ; 2020INFO019.
Deep neural networks learn representations of data to facilitate problem-solving in their respective domains. However, they struggle to acquire a structured representation based on more symbolic entities, which are commonly understood as core abstractions central to human capacity for generalization. This dissertation studies this issue for visual reasoning tasks. Inspired by how humans solve...
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Thèse de doctorat : Università della Svizzera italiana, 2018 ; 2018INFO006.
Artificial Neural Networks are increasingly being used in complex real- world applications because many-layered (i.e., deep) architectures can now be trained on large quantities of data. However, training even deeper, and therefore more powerful networks, has hit a barrier due to fundamental limitations in the design of existing networks. This thesis develops new architectures that, for the...
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Thèse de doctorat : Università della Svizzera italiana, 2016 ; 2016INFO004.
One day there will be humanoid robots among us doing our boring, time-consuming, or dangerous tasks. They might cook a delicious meal for us or do the groceries. For this to become reality, many advances need to be made to the artificial intelligence of humanoid robots. The ever-increasing available computational processing power opens new doors for such advances. In this thesis we develop...
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Thèse de doctorat : Università della Svizzera italiana, 2014 ; 2014INFO001.
Learning features for object detection and recognition with deep learning has received increasing attention in the past several years and recently attained widespread popularity. In this PhD thesis we investigate its applications to the automatic surface inspection system of our industrial partner ArcelorMittal, for classification and segmentation problems. Currently employed algorithms, in...
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