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09/26/2022
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
In principle, applying variational autoencoders (VAEs) to sequential dat...
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03/22/2021
Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation
In clinical practice, regions of interest in medical imaging often need ...
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03/16/2021
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
How to improve generative modeling by better exploiting spatial regulari...
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06/07/2019
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Learning meaningful and compact representations with structurally disent...
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06/07/2019
Disentangled State Space Representations
Sequential data often originates from diverse domains across which stati...
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10/31/2018
