1 citations · 1 across the 6 of their papers we have counts for
6 papers
Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies
Nitesh Kumar, Usashi Chatterjee, Steven Schockaert
Conceptual spaces represent entities in terms of their primitive semantic features. Such representations are highly valuable but they are notoriously difficult to learn, especially…
Solving Hard Analogy Questions with Relation Embedding Chains
Nitesh Kumar, Steven Schockaert
Modelling how concepts are related is a central topic in Lexical Semantics. A common strategy is to rely on knowledge graphs (KGs) such as ConceptNet, and to model the relation bet…
First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs
Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt
Statistical relational AI and probabilistic logic programming have so far mostly focused on discrete probabilistic models. The reasons for this is that one needs to provide constru…
Context-Specific Likelihood Weighting
Nitesh Kumar, Ondřej Kuželka
Sampling is a popular method for approximate inference when exact inference is impractical. Generally, sampling algorithms do not exploit context-specific independence (CSI) proper…
Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring
Pedro Zuidberg Dos Martires, Nitesh Kumar, Andreas Persson +2
Robotic agents should be able to learn from sub-symbolic sensor data, and at the same time, be able to reason about objects and communicate with humans on a symbolic level. This ra…
Learning Distributional Programs for Relational Autocompletion
Kumar Nitesh, Kuzelka Ondrej, De Raedt Luc
Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programmin…