Hierarchical Decompositional Mixtures of Variational Autoencoders
Autores:
Ping Liang Tan and Robert Peharz
Publicado en:
Proceedings of the 36th International Conference on Machine Learning (ICML), Edición 36, 2019, Página(s) 6115--6124
Editor:
Proceedings of Machine Learning Research
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
Autores:
Havasi, Marton; Peharz, Robert; Hernández-Lobato, José Miguel
Publicado en:
International Conference on Learning Representations, ICLR 2019, Edición 7, 2019, Página(s) --
Editor:
OpenReview.net
Faster Attend-Infer-Repeat with Tractable Probabilistic Models
Autores:
Karl Stelzner, Robert Peharz and Kristian Kersting
Publicado en:
Proceedings of the 36th International Conference on Machine Learning (ICML), Edición 36, 2019, Página(s) 5966--5975
Editor:
Proceedings of Machine Learning Research (PMLR)
Automatic Bayesian Density Analysis
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Autores:
Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera
Publicado en:
Proceedings of the AAAI Conference on Artificial Intelligence, Edición 33, 2019, Página(s) 5207-5215, ISSN 2374-3468
Editor:
AAAI Press
DOI:
10.1609/aaai.v33i01.33015207
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning
Autores:
Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Xiaoting Shao, Kristian Kersting and Zoubin Ghahramani
Publicado en:
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, (UAI), Edición 35, 2019, Página(s) --
Editor:
AUAI Press
Bayesian Learning of Sum-Product Networks
Autores:
Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani
Publicado en:
Advances in Neural Information Processing Systems, Edición 32, 2019, Página(s) 6344--6355
Editor:
Curran Associates, Inc.
Deep Structured Mixtures of Gaussian Processes
Autores:
Martin Trapp, Robert Peharz, Franz Pernkopf, Carl E. Rasmussen
Publicado en:
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Edición 1, 2020, Página(s) accepted, in print
Editor:
Proceedings of Machine Learning Research
