Sum-product networks: A new deep architecture
The key limiting factor in graphical model inference and learning is the complexity of the
partition function. We thus ask the question: what are the most general conditions under …
partition function. We thus ask the question: what are the most general conditions under …
Learning the structure of sum-product networks
Sum-product networks (SPNs) are a new class of deep probabilistic models. SPNs can have
unbounded treewidth but inference in them is always tractable. An SPN is either a univariate …
unbounded treewidth but inference in them is always tractable. An SPN is either a univariate …
Compositional convolutional neural networks: A robust and interpretable model for object recognition under occlusion
Computer vision systems in real-world applications need to be robust to partial occlusion
while also being explainable. In this work, we show that black-box deep convolutional …
while also being explainable. In this work, we show that black-box deep convolutional …
On probabilistic inference by weighted model counting
M Chavira, A Darwiche - Artificial Intelligence, 2008 - Elsevier
A recent and effective approach to probabilistic inference calls for reducing the problem to
one of weighted model counting (WMC) on a propositional knowledge base. Specifically, the …
one of weighted model counting (WMC) on a propositional knowledge base. Specifically, the …
Foundations of spatial perception for robotics: Hierarchical representations and real-time systems
3D spatial perception is the problem of building and maintaining an actionable and
persistent representation of the environment in real-time using sensor data and prior …
persistent representation of the environment in real-time using sensor data and prior …
Learning optimal decision trees using constraint programming
Decision trees are among the most popular classification models in machine learning.
Traditionally, they are learned using greedy algorithms. However, such algorithms pose …
Traditionally, they are learned using greedy algorithms. However, such algorithms pose …
Probabilistic theorem proving
Many representation schemes combining first-order logic and probability have been
proposed in recent years. Progress in unifying logical and probabilistic inference has been …
proposed in recent years. Progress in unifying logical and probabilistic inference has been …
[PDF][PDF] Lifted probabilistic inference by first-order knowledge compilation
Probabilistic logical languages provide powerful formalisms for knowledge representation
and learning. Yet performing inference in these languages is extremely costly, especially if it …
and learning. Yet performing inference in these languages is extremely costly, especially if it …
Cutset networks: A simple, tractable, and scalable approach for improving the accuracy of chow-liu trees
In this paper, we present cutset networks, a new tractable probabilistic model for
representing multi-dimensional discrete distributions. Cutset networks are rooted OR search …
representing multi-dimensional discrete distributions. Cutset networks are rooted OR search …
[書籍][B] Reasoning with probabilistic and deterministic graphical models: Exact algorithms
R Dechter - 2022 - books.google.com
Graphical models (eg, Bayesian and constraint networks, influence diagrams, and Markov
decision processes) have become a central paradigm for knowledge representation and …
decision processes) have become a central paradigm for knowledge representation and …