32 citations · 37 across the 4 of their papers we have counts for
14 papers
Towards Robust Classification with Deep Generative Forests
Alvaro H. C. Correia, Robert Peharz, Cassio de Campos
Decision Trees and Random Forests are among the most widely used machine learning models, and often achieve state-of-the-art performance in tabular, domain-agnostic datasets. Nonet…
Joints in Random Forests
Alvaro H. C. Correia, Robert Peharz, Cassio de Campos
Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist.…
Sum-Product Network Decompilation
Cory J. Butz, Jhonatan S. Oliveira, Robert Peharz
There exists a dichotomy between classical probabilistic graphical models, such as Bayesian networks (BNs), and modern tractable models, such as sum-product networks (SPNs). The fo…
Deep Structured Mixtures of Gaussian Processes
Martin Trapp, Robert Peharz, Franz Pernkopf +1
Gaussian Processes (GPs) are powerful non-parametric Bayesian regression models that allow exact posterior inference, but exhibit high computational and memory costs. In order to i…
Optimisation of Overparametrized Sum-Product Networks
Martin Trapp, Robert Peharz, Franz Pernkopf
It seems to be a pearl of conventional wisdom that parameter learning in deep sum-product networks is surprisingly fast compared to shallow mixture models. This paper examines the…
Bayesian Learning of Sum-Product Networks
Martin Trapp, Robert Peharz, Hong Ge +2
Sum-product networks (SPNs) are flexible density estimators and have received significant attention due to their attractive inference properties. While parameter learning in SPNs i…