Alejandro Molina

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 38

Top Topics

Probabilistic Model

Approximate Query Processing

Vehicular Traffic

Top Venues

Publications

Quentin Delfosse, Patrick Schramowski, Martin Mundt, Alejandro Molina, Kristian Kersting
Adaptive Rational Activations to Boost Deep Reinforcement Learning. ICLR (2024)
Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, Matthias Hollick
Next2You: Robust Copresence Detection Based on Channel State Information. ACM Trans. Internet Things 3 (2) (2022)
Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting
Conditional sum-product networks: Modular probabilistic circuits via gate functions. Int. J. Approx. Reason. 140 (2022)
Quentin Delfosse, Patrick Schramowski, Alejandro Molina, Kristian Kersting
Recurrent Rational Networks. CoRR (2021)
Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, Matthias Hollick
Next2You: Robust Copresence Detection Based on Channel State Information. CoRR (2021)
Amos Treiber, Alejandro Molina, Christian Weinert, Thomas Schneider, Kristian Kersting
CryptoSPN: Privacy-Preserving Sum-Product Network Inference. ECAI (2020)
Fabrizio Ventola, Karl Stelzner, Alejandro Molina, Kristian Kersting
Residual Sum-Product Networks. PGM (2020)
Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. CoRR (2020)
Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures. PGM (2020)
Amos Treiber, Alejandro Molina, Christian Weinert, Thomas Schneider, Kristian Kersting
CryptoSPN: Expanding PPML beyond Neural Networks. PPMLP@CCS (2020)
Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. ICML (2020)
Alejandro Molina, Patrick Schramowski, Kristian Kersting
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks. ICLR (2020)
Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa, Alejandro Molina, Kristian Kersting, Carsten Binnig
DeepDB: Learn from Data, not from Queries! Proc. VLDB Endow. 13 (7) (2020)
Amos Treiber, Alejandro Molina, Christian Weinert, Thomas Schneider, Kristian Kersting
CryptoSPN: Privacy-preserving Sum-Product Network Inference. CoRR (2020)
Alejandro Molina, Patrick Schramowski, Kristian Kersting
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks. CoRR (2019)
Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick
Perils of Zero-Interaction Security in the Internet of Things. CoRR (2019)
Lukas Weber, Lukas Sommer, Julian Oppermann, Alejandro Molina, Kristian Kersting, Andreas Koch
Resource-Efficient Logarithmic Number Scale Arithmetic for SPN Inference on FPGAs. FPT (2019)
Claas Völcker, Alejandro Molina, Johannes Neumann, Dirk Westermann, Kristian Kersting
DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets. PKDD/ECML Workshops (1) (2019)
Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick
Perils of Zero-Interaction Security in the Internet of Things. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3 (1) (2019)
Fabrizio Ventola, Karl Stelzner, Alejandro Molina, Kristian Kersting
Random Sum-Product Forests with Residual Links. CoRR (2019)
Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Xiaoting Shao, Kristian Kersting, Zoubin Ghahramani
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning. UAI (2019)
Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera
Automatic Bayesian Density Analysis. AAAI (2019)
Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures. CoRR (2019)
Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Pranav Subramani, Nicola Di Mauro, Pascal Poupart, Kristian Kersting
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks. CoRR (2019)
Stefan Lüdtke, Alejandro Molina, Kristian Kersting, Thomas Kirste
Gaussian Lifted Marginal Filtering. KI (2019)
Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa, Alejandro Molina, Kristian Kersting, Carsten Binnig
DeepDB: Learn from Data, not from Queries! CoRR (2019)
Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani
Probabilistic Deep Learning using Random Sum-Product Networks. CoRR (2018)
Antonio Vergari, Robert Peharz, Nicola Di Mauro, Alejandro Molina, Kristian Kersting, Floriana Esposito
Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks. AAAI (2018)
Moritz Kulessa, Alejandro Molina, Carsten Binnig, Benjamin Hilprecht, Kristian Kersting
Model-based Approximate Query Processing. CoRR (2018)
Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting
Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains. AAAI (2018)
Alejandro Molina, Alexander Munteanu, Kristian Kersting
Core Dependency Networks. AAAI (2018)
Stefan Lüdtke, Alejandro Molina, Thomas Kirste
Gaussian Lifted Marginal Filtering. iWOAR (2018)
Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera
Automatic Bayesian Density Analysis. CoRR (2018)
Lukas Sommer, Julian Oppermann, Alejandro Molina, Carsten Binnig, Kristian Kersting, Andreas Koch
Automatic Mapping of the Sum-Product Network Inference Problem to FPGA-Based Accelerators. ICCD (2018)
Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting
Sum-Product Networks for Hybrid Domains. CoRR (2017)
Alejandro Molina, Sriraam Natarajan, Kristian Kersting
Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions. AAAI (2017)
Alejandro Molina, Alexander Munteanu, Kristian Kersting
Coresets for Dependency Networks. CoRR (2017)
Elena Erdmann, Karin Boczek, Lars Koppers, Gerret von Nordheim, Christian Pölitz, Alejandro Molina, Katharina Morik, Henrik Müller, Jörg Rahnenführer, Kristian Kersting
Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage. CoRR (2016)
Christoph Ide, Fabian Hadiji, Lars Habel, Alejandro Molina, Thomas Zaksek, Michael Schreckenberg, Kristian Kersting, Christian Wietfeld
LTE Connectivity and Vehicular Traffic Prediction Based on Machine Learning Approaches. VTC Fall (2015)
Fabian Hadiji, Alejandro Molina, Sriraam Natarajan, Kristian Kersting
Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data. Mach. Learn. 100 (2-3) (2015)