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Found 7 papers, 3 papers with code
Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies
We focus in particular on the task of ranking entities according to a given conceptual space dimension.
Solving Hard Analogy Questions with Relation Embedding Chains
A common strategy is to rely on knowledge graphs (KGs) such as ConceptNet, and to model the relation between two concepts as a set of paths.
Hybrid Probabilistic Logic Programming: Inference and Learning
Next, a new hybrid PLP, DC#, is introduced, which integrates the syntax of Distributional Clauses with Bayesian logic programs and represents three types of independencies: i) conditional independencies (CIs) modeled in Bayesian networks; ii) context-specific independencies (CSIs) represented by logical rules, and iii) independencies amongst attributes of related objects in relational models expressed by combining rules.
Multimodal Neural Network For Demand Forecasting
Demand forecasting applications have immensely benefited from the state-of-the-art Deep Learning methods used for time series forecasting.
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.
Context-Specific Likelihood Weighting
Sampling is a popular method for approximate inference when exact inference is impractical.
Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring
To validate our approach we demonstrate, on the one hand, the ability of our system to perform probabilistic reasoning over multi-modal probability distributions, and on the other hand, the learning of probabilistic logical rules from anchored objects produced by perceptual observations.
