32 citations · 70 across the 6 of their papers we have counts for
6 papers · 1 filter
DeepDB: Learn from Data, not from Queries!
Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa +3
The typical approach for learned DBMS components is to capture the behavior by running a representative set of queries and use the observations to train a machine learning model. T…
Random Sum-Product Forests with Residual Links
Fabrizio Ventola, Karl Stelzner, Alejandro Molina +1
Tractable yet expressive density estimators are a key building block of probabilistic machine learning. While sum-product networks (SPNs) offer attractive inference capabilities, o…
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks
Alejandro Molina, Patrick Schramowski, Kristian Kersting
The performance of deep network learning strongly depends on the choice of the non-linear activation function associated with each neuron. However, deciding on the best activation…
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
Xiaoting Shao, Alejandro Molina, Antonio Vergari +4
Probabilistic graphical models are a central tool in AI; however, they are generally not as expressive as deep neural models, and inference is notoriously hard and slow. In contras…
Perils of Zero-Interaction Security in the Internet of Things
Mikhail Fomichev, Max Maass, Lars Almon +2
The Internet of Things (IoT) demands authentication systems which can provide both security and usability. Recent research utilizes the rich sensing capabilities of smart devices t…
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks
Alejandro Molina, Antonio Vergari, Karl Stelzner +5
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…