32 citations · 38 across the 5 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…
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato
While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage re…
Automatic Bayesian Density Analysis
Antonio Vergari, Alejandro Molina, Robert Peharz +3
Making sense of a dataset in an automatic and unsupervised fashion is a challenging problem in statistics and AI. Classical approaches for {exploratory data analysis} are usually n…
Safe Semi-Supervised Learning of Sum-Product Networks
Martin Trapp, Tamas Madl, Robert Peharz +2
In several domains obtaining class annotations is expensive while at the same time unlabelled data are abundant. While most semi-supervised approaches enforce restrictive assumptio…