Abstract: This paper presents SILEA (a System for Inductive LEArning), an efficient inductive learning algorithm for rule extraction. SILEA is a covering algorithm ...

Inductive logic programming (ILP) and machine learning together represent a powerful synthesis of symbolic reasoning and statistical inference. ILP focuses on deriving interpretable logic rules from ...

Machine learning research aims to learn representations that enable effective downstream task performance. A growing subfield seeks to interpret these representations’ roles in model behaviors or ...

Inductive logic programming [24] is situated in the intersection of machine learning or data mining on the one hand, and logic programming on the other hand. It shares with the former fields the goal ...

Graph Transformers (GTs) have successfully achieved state-of-the-art performance on various platforms. GTs can capture long-range information from nodes that are at large distances, unlike the local ...

[IEEE SP'24] The Official Implementation of "Jbeil: Temporal Graph-Based Inductive Learning to Infer Lateral Movement in Evolving Enterprise Networks" ...

Abstract: Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG), where entities are composed of free-form text. Existing CKG completion methods focus on transductive learning ...

Dr. Tehseen Zia has Doctorate and more than 10 years of post-Doctorate research experience in Artificial Intelligence (AI). He is assistant professor and leads AI… In machine learning, inductive bias ...

Learning from a limited number of experiences requires suitable inductive biases. To identify how inductive biases are implemented in and shaped by neural codes, we analyze sample-efficient learning ...

Contrastive learning is a popular form of self-supervised learning that encourages augmentations (views) of the same input to have more similar representations compared to augmentations of different ...