Robert Peharz

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 49

Top Topics

Gaussian Processes

Generative Model

Top Venues

Publications

Tim D'Hondt, Mykola Pechenizkiy, Robert Peharz
One-Shot Federated Learning with Bayesian Pseudocoresets. CoRR (2024)
Gennaro Gala, Cassio P. de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur
Probabilistic Integral Circuits. AISTATS (2024)
Christian Toth, Christian Knoll, Franz Pernkopf, Robert Peharz
Rao-Blackwellising Bayesian Causal Inference. CoRR (2024)
Yang Yang, Gennaro Gala, Robert Peharz
Bayesian Structure Scores for Probabilistic Circuits. CoRR (2023)
Gennaro Gala, Cassio P. de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur
Probabilistic Integral Circuits. CoRR (2023)
Lorenzo Loconte, Nicola Di Mauro, Robert Peharz, Antonio Vergari
How to Turn Your Knowledge Graph Embeddings into Generative Models. NeurIPS (2023)
Alvaro H. C. Correia, Gennaro Gala, Erik Quaeghebeur, Cassio P. de Campos, Robert Peharz
Continuous Mixtures of Tractable Probabilistic Models. AAAI (2023)
Yang Yang, Gennaro Gala, Robert Peharz
Bayesian Structure Scores for Probabilistic Circuits. AISTATS (2023)
Lorenzo Loconte, Nicola Di Mauro, Robert Peharz, Antonio Vergari
How to Turn Your Knowledge Graph Embeddings into Generative Models via Probabilistic Circuits. CoRR (2023)
Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen
Active Bayesian Causal Inference. CoRR (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)
Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen
Active Bayesian Causal Inference. NeurIPS (2022)
Alvaro H. C. Correia, Gennaro Gala, Erik Quaeghebeur, Cassio P. de Campos, Robert Peharz
Continuous Mixtures of Tractable Probabilistic Models. CoRR (2022)
Steven Lang, Martin Mundt, Fabrizio Ventola, Robert Peharz, Kristian Kersting
Elevating Perceptual Sample Quality in PCs through Differentiable Sampling. Pre-Registration Workshop @ NeurIPS (2021)
Cory J. Butz, Jhonatan de S. Oliveira, Robert Peharz
Sum-Product Network Decompilation. PGM (2020)
Alvaro H. C. Correia, Robert Peharz, Cassio P. de Campos
Joints in Random Forests. CoRR (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)
Alvaro H. C. Correia, Robert Peharz, Cassio P. de Campos
Joints in Random Forests. NeurIPS (2020)
Martin Trapp, Robert Peharz, Franz Pernkopf, Carl Edward Rasmussen
Deep Structured Mixtures of Gaussian Processes. AISTATS (2020)
Ricky Maulana Fajri, Samaneh Khoshrou, Robert Peharz, Mykola Pechenizkiy
PS3: Partition-Based Skew-Specialized Sampling for Batch Mode Active Learning in Imbalanced Text Data. ECML/PKDD (5) (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)
Wolfgang Roth, Günther Schindler, Matthias Zöhrer, Lukas Pfeifenberger, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani
Resource-Efficient Neural Networks for Embedded Systems. CoRR (2020)
Alvaro H. C. Correia, Robert Peharz, Cassio P. de Campos
Towards Robust Classification with Deep Generative Forests. CoRR (2020)
Cory J. Butz, Jhonatan de S. Oliveira, Robert Peharz
Sum-Product Network Decompilation. CoRR (2019)
Karl Stelzner, Robert Peharz, Kristian Kersting
Faster Attend-Infer-Repeat with Tractable Probabilistic Models. ICML (2019)
Ping Liang Tan, Robert Peharz
Hierarchical Decompositional Mixtures of Variational Autoencoders. ICML (2019)
Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani
Bayesian Learning of Sum-Product Networks. NeurIPS (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)
Martin Trapp, Robert Peharz, Franz Pernkopf
Optimisation of Overparametrized Sum-Product Networks. CoRR (2019)
Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters. ICLR (Poster) (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)
Martin Trapp, Robert Peharz, Franz Pernkopf, Carl E. Rasmussen
Deep Structured Mixtures of Gaussian Processes. CoRR (2019)
Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani
Bayesian Learning of Sum-Product Networks. CoRR (2019)
Martin Trapp, Robert Peharz, Carl E. Rasmussen, Franz Pernkopf
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks. CoRR (2018)
Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani
Probabilistic Deep Learning using Random Sum-Product Networks. CoRR (2018)
Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters. 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)
Wolfgang Roth, Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf
Hybrid generative-discriminative training of Gaussian mixture models. Pattern Recognit. Lett. 112 (2018)
Franz Pernkopf, Wolfgang Roth, Matthias Zöhrer, Lukas Pfeifenberger, Günther Schindler, Holger Fröning, Sebastian Tschiatschek, Robert Peharz, Matthew Mattina, Zoubin Ghahramani
Efficient and Robust Machine Learning for Real-World Systems. CoRR (2018)
Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera
Automatic Bayesian Density Analysis. CoRR (2018)
Martin Trapp, Tamas Madl, Robert Peharz, Franz Pernkopf, Robert Trappl
Safe Semi-Supervised Learning of Sum-Product Networks. UAI (2017)
Antonio Vergari, Robert Peharz, Nicola Di Mauro, Floriana Esposito
Encoding and Decoding Representations with Sum- and Max-Product Networks. ICLR (Workshop) (2017)
Robert Peharz, Robert Gens, Franz Pernkopf, Pedro M. Domingos
On the Latent Variable Interpretation in Sum-Product Networks. IEEE Trans. Pattern Anal. Mach. Intell. 39 (10) (2017)
Martin Trapp, Tamas Madl, Robert Peharz, Franz Pernkopf, Robert Trappl
Safe Semi-Supervised Learning of Sum-Product Networks. CoRR (2017)
Robert Peharz, Robert Gens, Franz Pernkopf, Pedro M. Domingos
On the Latent Variable Interpretation in Sum-Product Networks. CoRR (2016)
Florian B. Pokorny, Robert Peharz, Wolfgang Roth, Matthias Zöhrer, Franz Pernkopf, Peter B. Marschik, Björn W. Schuller
Manual versus Automated: The Challenging Routine of Infant Vocalisation Segmentation in Home Videos to Study Neuro(mal)development. INTERSPEECH (2016)
Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf, Pedro M. Domingos
On Theoretical Properties of Sum-Product Networks. AISTATS (2015)
Matthias Zöhrer, Robert Peharz, Franz Pernkopf
On representation learning for artificial bandwidth extension. INTERSPEECH (2015)
Matthias Zöhrer, Robert Peharz, Franz Pernkopf
Representation Learning for Single-Channel Source Separation and Bandwidth Extension. IEEE ACM Trans. Audio Speech Lang. Process. 23 (12) (2015)
Robert Peharz, Georg Kapeller, Pejman Mowlaee, Franz Pernkopf
Modeling speech with sum-product networks: Application to bandwidth extension. ICASSP (2014)
Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf
The Most Generative Maximum Margin Bayesian Networks. ICML (3) (2013)
Robert Peharz, Bernhard C. Geiger, Franz Pernkopf
Greedy Part-Wise Learning of Sum-Product Networks. ECML/PKDD (2) (2013)
Robert Peharz, Franz Pernkopf
-constraints. Neurocomputing 80 (2012)
Robert Peharz, Franz Pernkopf
On linear and mixmax interaction models for single channel source separation. ICASSP (2012)
Robert Peharz, Franz Pernkopf
Exact Maximum Margin Structure Learning of Bayesian Networks. ICML (2012)
Robert Peharz, Michael Wohlmayr, Franz Pernkopf
Gain-robust multi-pitch tracking using sparse nonnegative matrix factorization. ICASSP (2011)
Michael Wohlmayr, Robert Peharz, Franz Pernkopf
Efficient implementation of probabilistic multi-pitch tracking. ICASSP (2011)
Robert Peharz, Michael Stark, Franz Pernkopf, Yannis Stylianou
A factorial sparse coder model for single channel source separation. INTERSPEECH (2010)