4 code implementations • 18 Feb 2021 • Quentin Delfosse, Patrick Schramowski, Martin Mundt, Alejandro Molina, Kristian Kersting
Latest insights from biology show that intelligence not only emerges from the connections between neurons but that individual neurons shoulder more computational responsibility than previously anticipated.
Ranked #3 on Atari Games on Atari 2600 Skiing (using extra training data)
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.
no code implementations • 3 Feb 2020 • Amos Treiber, Alejandro Molina, Christian Weinert, Thomas Schneider, Kristian Kersting
AI algorithms, and machine learning (ML) techniques in particular, are increasingly important to individuals' lives, but have caused a range of privacy concerns addressed by, e. g., the European GDPR.
1 code implementation • 2 Sep 2019 • Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa, Alejandro Molina, Kristian Kersting, Carsten Binnig
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.
Databases
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.
5 code implementations • ICLR 2020 • 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.
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 • 22 Jan 2019 • Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick
The Internet of Things (IoT) demands authentication systems which can provide both security and usability.
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 • 15 Nov 2018 • Moritz Kulessa, Alejandro Molina, Carsten Binnig, Benjamin Hilprecht, Kristian Kersting
However, classical AQP approaches suffer from various problems that limit the applicability to support the ad-hoc exploration of a new data set: (1) Classical AQP approaches that perform online sampling can support ad-hoc exploration queries but yield low quality if executed over rare subpopulations.
no code implementations • 24 Jul 2018 • Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera
Classical approaches for {exploratory data analysis} are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference.
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.
no code implementations • 9 Oct 2017 • Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting
While all kinds of mixed data -from personal data, over panel and scientific data, to public and commercial data- are collected and stored, building probabilistic graphical models for these hybrid domains becomes more difficult.
no code implementations • 9 Oct 2017 • Alejandro Molina, Alexander Munteanu, Kristian Kersting
Many applications infer the structure of a probabilistic graphical model from data to elucidate the relationships between variables.
no code implementations • 21 Feb 2017 • Luis Adrián Cabrera-Diego, Stéphane Huet, Bassam Jabaian, Alejandro Molina, Juan-Manuel Torres-Moreno, Marc El-Bèze, Barthélémy Durette
This year, the DEFT campaign (D\'efi Fouilles de Textes) incorporates a task which aims at identifying the session in which articles of previous TALN conferences were presented.
no code implementations • 16 Jun 2016 • Elena Erdmann, Karin Boczek, Lars Koppers, Gerret von Nordheim, Christian Pölitz, Alejandro Molina, Katharina Morik, Henrik Müller, Jörg Rahnenführer, Kristian Kersting
Migration crisis, climate change or tax havens: Global challenges need global solutions.
no code implementations • 20 Jan 2015 • Gerardo Sierra, Juan-Manuel Torres-Moreno, Alejandro Molina
This article focuses on the description and evaluation of a new unsupervised learning method of clustering of definitions in Spanish according to their semantic.