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Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods
One of the reasons for the success of convolutional networks is their eq...
10/31/2019 ∙ by Luca Della Libera, et al. ∙
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Learning to Evolve
Evolution and learning are two of the fundamental mechanisms by which li...
05/08/2019 ∙ by Jan Schuchardt, et al. ∙
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q-Space Novelty Detection with Variational Autoencoders
In machine learning, novelty detection is the task of identifying novel ...
06/08/2018 ∙ by Aleksei Vasilev, et al. ∙
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Clustering with Deep Learning: Taxonomy and New Methods
Clustering is a fundamental machine learning method. The quality of its ...
01/23/2018 ∙ by Elie Aljalbout, et al. ∙
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Regularization for Deep Learning: A Taxonomy
Regularization is one of the crucial ingredients of deep learning, yet t...
10/29/2017 ∙ by Jan Kukačka, et al. ∙
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3D Deep Learning for Biological Function Prediction from Physical Fields
Predicting the biological function of molecules, be it proteins or drug-...
04/13/2017 ∙ by Vladimir Golkov, et al. ∙
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FlowNet: Learning Optical Flow with Convolutional Networks
Convolutional neural networks (CNNs) have recently been very successful ...
04/26/2015 ∙ by Philipp Fischer, et al. ∙
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Vladimir Golkov
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TU München
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Technische Universität München
Doctoral Candidate at Technical University of Munich, Doctoral Candidate at GE Global Research from 2012-2014, Intern at GE Global Research from 2011-2012, Intern at Munich Airport from 2009-2010 Sofware Development
