17 citations · 78 across the 17 of their papers we have counts for
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cs.LG2019★ 16 cited
Learning under Model Misspecification: Applications to Variational and Ensemble methods
Andres R. Masegosa
Virtually any model we use in machine learning to make predictions does not perfectly represent reality. So, most of the learning happens under model misspecification. In this work…
cs.LG2019
Probabilistic Models with Deep Neural Networks
Andrés R. Masegosa, Rafael Cabañas, Helge Langseth +2
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to (i) very re…
cs.LG2019
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy
Javier Cózar, Rafael Cabañas, Antonio Salmerón +1
InferPy is a Python package for probabilistic modeling with deep neural networks. It defines a user-friendly API that trades-off model complexity with ease of use, unlike other lib…