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Found 22 papers, 7 papers with code
First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs
Three types of independencies are important to represent and exploit for scalable inference in hybrid models: conditional independencies elegantly modeled in Bayesian networks, context-specific independencies naturally represented by logical rules, and independencies amongst attributes of related objects in relational models succinctly expressed by combining rules.
Learning with Molecules beyond Graph Neural Networks
We demonstrate a deep learning framework which is inherently based in the highly expressive language of relational logic, enabling to, among other things, capture arbitrarily complex graph structures.
Beyond Graph Neural Networks with Lifted Relational Neural Networks
We demonstrate a declarative differentiable programming framework based on the language of Lifted Relational Neural Networks, where small parameterized logic programs are used to encode relational learning scenarios.
Lossless Compression of Structured Convolutional Models via Lifting
The computation graphs themselves then reflect the symmetries of the underlying data, similarly to the lifted graphical models.
Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers
It is known due to the work of Van den Broeck et al [KR, 2014] that weighted first-order model counting (WFOMC) in the two-variable fragment of first-order logic can be solved in time polynomial in the number of domain elements.
Lifted Inference in 2-Variable Markov Logic Networks with Function and Cardinality Constraints Using Discrete Fourier Transform
In this paper we show that inference in 2-variable Markov logic networks (MLNs) with cardinality and function constraints is domain-liftable.
Complex Markov Logic Networks: Expressivity and Liftability
We study expressivity of Markov logic networks (MLNs).
Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry
We study the symmetric weighted first-order model counting task and present ApproxWFOMC, a novel anytime method for efficiently bounding the weighted first-order model count in the presence of an unweighted first-order model counting oracle.
Domain-Liftability of Relational Marginal Polytopes
We study computational aspects of relational marginal polytopes which are statistical relational learning counterparts of marginal polytopes, well-known from probabilistic graphical models.
Knowledge Graph Embedding: A Probabilistic Perspective and Generalization Bounds
We study theoretical properties of embedding methods for knowledge graph completion under the missing completely at random assumption.
Lifted Weight Learning of Markov Logic Networks Revisited
We study lifted weight learning of Markov logic networks.
Scalable Rule Learning in Probabilistic Knowledge Bases
In this paper, we present SafeLearner -- a scalable solution to probabilistic KB completion that performs probabilistic rule learning using lifted probabilistic inference -- as faster approach instead of grounding.
Quantified Markov Logic Networks
Markov Logic Networks (MLNs) are well-suited for expressing statistics such as "with high probability a smoker knows another smoker" but not for expressing statements such as "there is a smoker who knows most other smokers", which is necessary for modeling, e. g. influencers in social networks.
VC-Dimension Based Generalization Bounds for Relational Learning
In many applications of relational learning, the available data can be seen as a sample from a larger relational structure (e. g. we may be given a small fragment from some social network).
PAC-Reasoning in Relational Domains
We consider the problem of predicting plausible missing facts in relational data, given a set of imperfect logical rules.
Stacked Structure Learning for Lifted Relational Neural Networks
Lifted Relational Neural Networks (LRNNs) describe relational domains using weighted first-order rules which act as templates for constructing feed-forward neural networks.
Relational Marginal Problems: Theory and Estimation
In the propositional setting, the marginal problem is to find a (maximum-entropy) distribution that has some given marginals.
Induction of Interpretable Possibilistic Logic Theories from Relational Data
Compared to Markov Logic Networks (MLNs), our method is faster and produces considerably more interpretable models.
Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract)
In this paper, we advocate the use of stratified logical theories for representing probabilistic models.
Learning Possibilistic Logic Theories from Default Rules
We introduce a setting for learning possibilistic logic theories from defaults of the form "if alpha then typically beta".
Lifted Relational Neural Networks
We propose a method combining relational-logic representations with neural network learning.
Encoding Markov Logic Networks in Possibilistic Logic
Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds.
