Robert Peharz

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Continuous Mixtures of Tractable Probabilistic Models.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Conditional sum-product networks: Modular probabilistic circuits via gate functions.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2022

Active Bayesian Causal Inference.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Elevating Perceptual Sample Quality in PCs through Differentiable Sampling.

[BibT_eX]

[DOI]

Proceedings of the NeurIPS 2021 Workshop on Pre-Registration in Machine Learning, 2021

2020

Towards Robust Classification with Deep Generative Forests.

[BibT_eX]

[DOI]

Alvaro H. C. Correia

Cassio P. de Campos

CoRR, 2020

Resource-Efficient Neural Networks for Embedded Systems.

[BibT_eX]

[DOI]

Holger Fröning

Zoubin Ghahramani

CoRR, 2020

PS3: Partition-Based Skew-Specialized Sampling for Batch Mode Active Learning in Imbalanced Text Data.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 2020

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

[BibT_eX]

[DOI]

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Sum-Product Network Decompilation.

[BibT_eX]

[DOI]

Cory J. Butz

Jhonatan de S. Oliveira

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Joints in Random Forests.

[BibT_eX]

[DOI]

Alvaro H. C. Correia

Cassio P. de Campos

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Deep Structured Mixtures of Gaussian Processes.

[BibT_eX]

[DOI]

Martin Trapp

Carl Edward Rasmussen

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Optimisation of Overparametrized Sum-Product Networks.

[BibT_eX]

[DOI]

Martin Trapp

CoRR, 2019

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

[BibT_eX]

[DOI]

CoRR, 2019

Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Bayesian Learning of Sum-Product Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Hierarchical Decompositional Mixtures of Variational Autoencoders.

[BibT_eX]

[DOI]

Ping Liang Tan

Proceedings of the 36th International Conference on Machine Learning, 2019

Faster Attend-Infer-Repeat with Tractable Probabilistic Models.

[BibT_eX]

[DOI]

Karl Stelzner

Kristian Kersting

Proceedings of the 36th International Conference on Machine Learning, 2019

Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters.

[BibT_eX]

[DOI]

Marton Havasi

José Miguel Hernández-Lobato

Proceedings of the 7th International Conference on Learning Representations, 2019

Automatic Bayesian Density Analysis.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Hybrid generative-discriminative training of Gaussian mixture models.

[BibT_eX]

[DOI]

Wolfgang Roth

Pattern Recognit. Lett., 2018

Efficient and Robust Machine Learning for Real-World Systems.

[BibT_eX]

[DOI]

Matthew Mattina

Zoubin Ghahramani

CoRR, 2018

Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks.

[BibT_eX]

[DOI]

CoRR, 2018

Probabilistic Deep Learning using Random Sum-Product Networks.

[BibT_eX]

[DOI]

CoRR, 2018

Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

On the Latent Variable Interpretation in Sum-Product Networks.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2017

Safe Semi-Supervised Learning of Sum-Product Networks.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Encoding and Decoding Representations with Sum- and Max-Product Networks.

[BibT_eX]

[DOI]

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Manual versus Automated: The Challenging Routine of Infant Vocalisation Segmentation in Home Videos to Study Neuro(mal)development.

[BibT_eX]

[DOI]

Proceedings of the Interspeech 2016, 2016

2015

Representation Learning for Single-Channel Source Separation and Bandwidth Extension.

[BibT_eX]

[DOI]

Matthias Zöhrer

IEEE ACM Trans. Audio Speech Lang. Process., 2015

On representation learning for artificial bandwidth extension.

[BibT_eX]

[DOI]

Matthias Zöhrer

Proceedings of the INTERSPEECH 2015, 2015

On Theoretical Properties of Sum-Product Networks.

[BibT_eX]

[DOI]

Pedro M. Domingos

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Modeling speech with sum-product networks: Application to bandwidth extension.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2014

2013

Greedy Part-Wise Learning of Sum-Product Networks.

[BibT_eX]

[DOI]

Bernhard C. Geiger

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

The Most Generative Maximum Margin Bayesian Networks.

[BibT_eX]

[DOI]

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Sparse nonnegative matrix factorization with ℓ<sup>0</sup>-constraints.

[BibT_eX]

[DOI]

Neurocomputing, 2012

Exact Maximum Margin Structure Learning of Bayesian Networks.

[BibT_eX]

[DOI]

Proceedings of the 29th International Conference on Machine Learning, 2012

On linear and mixmax interaction models for single channel source separation.

[BibT_eX]

[DOI]

Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

2011

Efficient implementation of probabilistic multi-pitch tracking.

[BibT_eX]

[DOI]

Michael Wohlmayr

Proceedings of the IEEE International Conference on Acoustics, 2011

Gain-robust multi-pitch tracking using sparse nonnegative matrix factorization.

[BibT_eX]

[DOI]

Michael Wohlmayr