Search Results for author: Alejandro Molina

Found 17 papers, 6 papers with code

Adaptive Rational Activations to Boost Deep Reinforcement Learning

3 code implementations18 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)

Atari Games General Reinforcement Learning +2

Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits

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.

CryptoSPN: Privacy-preserving Sum-Product Network Inference

no code implementations3 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.

Privacy Preserving

DeepDB: Learn from Data, not from Queries!

1 code implementation2 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

Random Sum-Product Forests with Residual Links

no code implementations8 Aug 2019 Fabrizio Ventola, Karl Stelzner, Alejandro Molina, Kristian Kersting

Tractable yet expressive density estimators are a key building block of probabilistic machine learning.

Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks

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.

Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures

no code implementations21 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.

Image Classification

Perils of Zero-Interaction Security in the Internet of Things

1 code implementation22 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.

SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks

1 code implementation11 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).

Model-based Approximate Query Processing

no code implementations15 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.

Automatic Bayesian Density Analysis

no code implementations24 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.

Anomaly Detection Bayesian Inference +1

Probabilistic Deep Learning using Random Sum-Product Networks

no code implementations5 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.

Probabilistic Deep Learning

Coresets for Dependency Networks

no code implementations9 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.

Sum-Product Networks for Hybrid Domains

no code implementations9 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.

Algorithmes de classification et d'optimisation: participation du LIA/ADOC á DEFT'14

no code implementations21 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.

General Classification

Regroupement sémantique de définitions en espagnol

no code implementations20 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.

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