11 citations · 11 across the 1 of their papers we have counts for
3 papers
Calibrated Adversarial Refinement for Stochastic Semantic Segmentation
Elias Kassapis, Georgi Dikov, Deepak K. Gupta +1
In semantic segmentation tasks, input images can often have more than one plausible interpretation, thus allowing for multiple valid labels. To capture such ambiguities, recent wor…
Bayesian Learning of Neural Network Architectures
Georgi Dikov, Patrick van der Smagt, Justin Bayer
In this paper we propose a Bayesian method for estimating architectural parameters of neural networks, namely layer size and network depth. We do this by learning concrete distribu…
3D Deep Learning for Biological Function Prediction from Physical Fields
Vladimir Golkov, Marcin J. Skwark, Atanas Mirchev +5
Predicting the biological function of molecules, be it proteins or drug-like compounds, from their atomic structure is an important and long-standing problem. Function is dictated…