32 citations · 172 across the 28 of their papers we have counts for
4 papers · 1 filter
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.…
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Robert Peharz, Steven Lang, Antonio Vergari +6
Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit a wide range of exact and efficient inference routines. Recent ``deep-learning-style'…
Resource-Efficient Neural Networks for Embedded Systems
Wolfgang Roth, Günther Schindler, Bernhard Klein +5
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-e…