376 citations · 670 across the 6 of their papers we have counts for
6 papers
Markov Logic in Infinite Domains
Parag Singla, Pedro Domingos
Combining first-order logic and probability has long been a goal of AI. Markov logic (Richardson & Domingos, 2006) accomplishes this by attaching weights to first-order formulas an…
Learning Arithmetic Circuits
Daniel Lowd, Pedro Domingos
Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, bec…
Formula-Based Probabilistic Inference
Vibhav Gogate, Pedro Domingos
Computing the probability of a formula given the probabilities or weights associated with other formulas is a natural extension of logical inference to the probabilistic setting. S…
Sum-Product Networks: A New Deep Architecture
Hoifung Poon, Pedro Domingos
The key limiting factor in graphical model inference and learning is the complexity of the partition function. We thus ask the question: what are general conditions under which the…
Probabilistic Theorem Proving
Vibhav Gogate, Pedro Domingos
Many representation schemes combining first-order logic and probability have been proposed in recent years. Progress in unifying logical and probabilistic inference has been slower…
Approximation by Quantization
Vibhav Gogate, Pedro Domingos
Inference in graphical models consists of repeatedly multiplying and summing out potentials. It is generally intractable because the derived potentials obtained in this way can be…