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ORCIDRobert Peharz
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- affiliation: Eindhoven University of Technology, The Netherlands
- affiliation: Graz University of Technology, Austria
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2020 – today
- 2024
[j6]Wolfgang Roth, Günther Schindler, Bernhard Klein, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani:
Resource-Efficient Neural Networks for Embedded Systems. J. Mach. Learn. Res. 25: 50:1-50:51 (2024)- [c33]Gennaro Gala, Cassio P. de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur:
Probabilistic Integral Circuits. AISTATS 2024: 2143-2151 - [c32]Thomas Wedenig, Rishub Nagpal, Gaëtan Cassiers, Stefan Mangard, Robert Peharz:
Exact Soft Analytical Side-Channel Attacks using Tractable Circuits. ICML 2024 - [i25]Christian Toth, Christian Knoll, Franz Pernkopf, Robert Peharz:
Rao-Blackwellising Bayesian Causal Inference. CoRR abs/2402.14781 (2024) - [i24]Tim D'Hondt, Mykola Pechenizkiy, Robert Peharz:
One-Shot Federated Learning with Bayesian Pseudocoresets. CoRR abs/2406.02177 (2024) - [i23]Lorenzo Loconte, Antonio Mari, Gennaro Gala, Robert Peharz, Cassio de Campos, Erik Quaeghebeur, Gennaro Vessio, Antonio Vergari:
What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)? CoRR abs/2409.07953 (2024) - 2023
- [c31]Alvaro H. C. Correia, Gennaro Gala, Erik Quaeghebeur
, Cassio P. de Campos, Robert Peharz:
Continuous Mixtures of Tractable Probabilistic Models. AAAI 2023: 7244-7252 - [c30]Yang Yang, Gennaro Gala, Robert Peharz:
Bayesian Structure Scores for Probabilistic Circuits. AISTATS 2023: 563-575 - [c29]Lorenzo Loconte, Nicola Di Mauro, Robert Peharz, Antonio Vergari:
How to Turn Your Knowledge Graph Embeddings into Generative Models. NeurIPS 2023 - [i22]Yang Yang, Gennaro Gala, Robert Peharz:
Bayesian Structure Scores for Probabilistic Circuits. CoRR abs/2302.12130 (2023) - [i21]Lorenzo Loconte, Nicola Di Mauro, Robert Peharz, Antonio Vergari:
How to Turn Your Knowledge Graph Embeddings into Generative Models via Probabilistic Circuits. CoRR abs/2305.15944 (2023) - [i20]Gennaro Gala, Cassio P. de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur:
Probabilistic Integral Circuits. CoRR abs/2310.16986 (2023) - 2022
- [j5]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: 298-313 (2022) - [c28]Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen:
Active Bayesian Causal Inference. NeurIPS 2022 - [i19]Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen:
Active Bayesian Causal Inference. CoRR abs/2206.02063 (2022) - [i18]Alvaro H. C. Correia, Gennaro Gala, Erik Quaeghebeur, Cassio P. de Campos, Robert Peharz:
Continuous Mixtures of Tractable Probabilistic Models. CoRR abs/2209.10584 (2022) - 2021
- [c27]Steven Lang, Martin Mundt, Fabrizio Ventola, Robert Peharz, Kristian Kersting:
Elevating Perceptual Sample Quality in PCs through Differentiable Sampling. Pre-Registration Workshop @ NeurIPS 2021: 1-25 - 2020
- [c26]Martin Trapp, Robert Peharz, Franz Pernkopf, Carl Edward Rasmussen:
Deep Structured Mixtures of Gaussian Processes. AISTATS 2020: 2251-2261 - [c25]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: 7563-7574 - [c24]Alvaro H. C. Correia, Robert Peharz, Cassio P. de Campos:
Joints in Random Forests. NeurIPS 2020 - [c23]Cory J. Butz, Jhonatan de S. Oliveira, Robert Peharz:
Sum-Product Network Decompilation. PGM 2020: 53-64 - [c22]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: 401-412 - [c21]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: 68-84 - [i17]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 abs/2001.03048 (2020) - [i16]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 abs/2004.06231 (2020) - [i15]Alvaro H. C. Correia, Robert Peharz, Cassio P. de Campos:
Joints in Random Forests. CoRR abs/2006.14937 (2020) - [i14]Alvaro H. C. Correia, Robert Peharz, Cassio P. de Campos:
Towards Robust Classification with Deep Generative Forests. CoRR abs/2007.05721 (2020)
2010 – 2019
- 2019
- [c20]Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera:
Automatic Bayesian Density Analysis. AAAI 2019: 5207-5215 - [c19]Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato:
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters. ICLR (Poster) 2019 - [c18]Karl Stelzner, Robert Peharz, Kristian Kersting:
Faster Attend-Infer-Repeat with Tractable Probabilistic Models. ICML 2019: 5966-5975 - [c17]Ping Liang Tan, Robert Peharz:
Hierarchical Decompositional Mixtures of Variational Autoencoders. ICML 2019: 6115-6124 - [c16]Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani:
Bayesian Learning of Sum-Product Networks. NeurIPS 2019: 6344-6355 - [c15]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: 334-344 - [i13]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 abs/1901.03704 (2019) - [i12]Martin Trapp, Robert Peharz, Franz Pernkopf:
Optimisation of Overparametrized Sum-Product Networks. CoRR abs/1905.08196 (2019) - [i11]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 abs/1905.08550 (2019) - [i10]Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani:
Bayesian Learning of Sum-Product Networks. CoRR abs/1905.10884 (2019) - [i9]Martin Trapp, Robert Peharz, Franz Pernkopf, Carl E. Rasmussen:
Deep Structured Mixtures of Gaussian Processes. CoRR abs/1910.04536 (2019) - [i8]Cory J. Butz, Jhonatan de S. Oliveira, Robert Peharz:
Sum-Product Network Decompilation. CoRR abs/1912.10092 (2019) - 2018
- [j4]Wolfgang Roth, Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf:
Hybrid generative-discriminative training of Gaussian mixture models. Pattern Recognit. Lett. 112: 131-137 (2018) - [c14]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: 4163-4170 - [i7]Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani:
Probabilistic Deep Learning using Random Sum-Product Networks. CoRR abs/1806.01910 (2018) - [i6]Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera:
Automatic Bayesian Density Analysis. CoRR abs/1807.09306 (2018) - [i5]Martin Trapp, Robert Peharz, Carl E. Rasmussen, Franz Pernkopf:
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks. CoRR abs/1809.04400 (2018) - [i4]Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato:
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters. CoRR abs/1810.00440 (2018) - [i3]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 abs/1812.02240 (2018) - 2017
- [j3]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): 2030-2044 (2017) - [c13]Antonio Vergari, Robert Peharz, Nicola Di Mauro, Floriana Esposito:
Encoding and Decoding Representations with Sum- and Max-Product Networks. ICLR (Workshop) 2017 - [c12]Martin Trapp, Tamas Madl, Robert Peharz, Franz Pernkopf, Robert Trappl:
Safe Semi-Supervised Learning of Sum-Product Networks. UAI 2017 - [i2]Martin Trapp, Tamas Madl, Robert Peharz, Franz Pernkopf, Robert Trappl:
Safe Semi-Supervised Learning of Sum-Product Networks. CoRR abs/1710.03444 (2017) - 2016
- [c11]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: 2997-3001 - [i1]Robert Peharz, Robert Gens, Franz Pernkopf, Pedro M. Domingos:
On the Latent Variable Interpretation in Sum-Product Networks. CoRR abs/1601.06180 (2016) - 2015
- [j2]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): 2398-2409 (2015) - [c10]Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf, Pedro M. Domingos:
On Theoretical Properties of Sum-Product Networks. AISTATS 2015 - [c9]Matthias Zöhrer, Robert Peharz, Franz Pernkopf:
On representation learning for artificial bandwidth extension. INTERSPEECH 2015: 791-795 - 2014
- [c8]Robert Peharz, Georg Kapeller, Pejman Mowlaee, Franz Pernkopf:
Modeling speech with sum-product networks: Application to bandwidth extension. ICASSP 2014: 3699-3703 - 2013
- [c7]Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf:
The Most Generative Maximum Margin Bayesian Networks. ICML (3) 2013: 235-243 - [c6]Robert Peharz, Bernhard C. Geiger, Franz Pernkopf:
Greedy Part-Wise Learning of Sum-Product Networks. ECML/PKDD (2) 2013: 612-627 - 2012
- [j1]Robert Peharz, Franz Pernkopf:
Sparse nonnegative matrix factorization with ℓ0-constraints. Neurocomputing 80: 38-46 (2012) - [c5]Robert Peharz, Franz Pernkopf:
On linear and mixmax interaction models for single channel source separation. ICASSP 2012: 249-252 - [c4]Robert Peharz, Franz Pernkopf:
Exact Maximum Margin Structure Learning of Bayesian Networks. ICML 2012 - 2011
- [c3]Michael Wohlmayr, Robert Peharz, Franz Pernkopf:
Efficient implementation of probabilistic multi-pitch tracking. ICASSP 2011: 5412-5415 - [c2]Robert Peharz, Michael Wohlmayr, Franz Pernkopf:
Gain-robust multi-pitch tracking using sparse nonnegative matrix factorization. ICASSP 2011: 5416-5419 - 2010
- [c1]Robert Peharz, Michael Stark, Franz Pernkopf, Yannis Stylianou:
A factorial sparse coder model for single channel source separation. INTERSPEECH 2010: 386-389
Coauthor Index
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