no code implementations • 1 Feb 2022 • Xiaoting Shao, Karl Stelzner, Kristian Kersting
A key assumption of most statistical machine learning methods is that they have access to independent samples from the distribution of data they encounter at test time.
1 code implementation • 2 Apr 2021 • Karl Stelzner, Kristian Kersting, Adam R. Kosiorek
We present ObSuRF, a method which turns a single image of a scene into a 3D model represented as a set of Neural Radiance Fields (NeRFs), with each NeRF corresponding to a different object.
1 code implementation • ICML 2020 • Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van Den Broeck, Kristian Kersting, Zoubin Ghahramani
Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit a wide range of exact and efficient inference routines.
1 code implementation • ICLR 2020 • Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, Kristian Kersting
When humans observe a physical system, they can easily locate objects, understand their interactions, and anticipate future behavior, even in settings with complicated and previously unseen interactions.
no code implementations • 8 Aug 2019 • Fabrizio Ventola, Karl Stelzner, Alejandro Molina, Kristian Kersting
Tractable yet expressive density estimators are a key building block of probabilistic machine learning.
no code implementations • 21 May 2019 • Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting
In contrast, deep probabilistic models such as sum-product networks (SPNs) capture joint distributions in a tractable fashion, but still lack the expressive power of intractable models based on deep neural networks.
1 code implementation • 11 Jan 2019 • Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Pranav Subramani, Nicola Di Mauro, Pascal Poupart, Kristian Kersting
We introduce SPFlow, an open-source Python library providing a simple interface to inference, learning and manipulation routines for deep and tractable probabilistic models called Sum-Product Networks (SPNs).
no code implementations • 5 Jun 2018 • Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani
The need for consistent treatment of uncertainty has recently triggered increased interest in probabilistic deep learning methods.