no code implementations • 23 Feb 2024 • Nitesh Kumar, Usashi Chatterjee, Steven Schockaert
We focus in particular on the task of ranking entities according to a given conceptual space dimension.
1 code implementation • 18 Oct 2023 • Nitesh Kumar, Steven Schockaert
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.
no code implementations • 1 Feb 2023 • Nitesh Kumar
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.
no code implementations • 20 Oct 2022 • Nitesh Kumar, Kumar Dheenadayalan, Suprabath Reddy, Sumant Kulkarni
Demand forecasting applications have immensely benefited from the state-of-the-art Deep Learning methods used for time series forecasting.
1 code implementation • 26 Jan 2022 • Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt
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.
1 code implementation • 24 Jan 2021 • Nitesh Kumar, Ondřej Kuželka
Sampling is a popular method for approximate inference when exact inference is impractical.
no code implementations • 24 Feb 2020 • Pedro Zuidberg Dos Martires, Nitesh Kumar, Andreas Persson, Amy Loutfi, Luc De Raedt
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.