Browsing by Advisor "Talukdar, Partha Pratim"
Now showing items 1-6 of 6
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Analysis and Methods for Knowledge Graph Embeddings
Knowledge Graphs (KGs) are multi-relational graphs where nodes represent entities, and typed edges represent relationships among entities. These graphs store real-world facts such as (Lionel Messi, plays-for-team, Barcelona) ... -
Deep Learning over Hypergraphs
Graphs have been extensively used for modelling real-world network datasets, however, they are restricted to pairwise relationships, i.e., each edge connects exactly two vertices. Hypergraphs relax the notion of edges ... -
Inducing Constraints in Paraphrase Generation and Consistency in Paraphrase Detection
Deep learning models typically require a large volume of data. Manual curation of datasets is time-consuming and limited by imagination. As a result, natural language generation (NLG) has been employed to automate the ... -
Leveraging KG Embeddings for Knowledge Graph Question Answering
Knowledge graphs (KG) are multi-relational graphs consisting of entities as nodes and relations among them as typed edges. The goal of knowledge graph question answering (KGQA) is to answer natural language queries posed ... -
Methods for Improving Data-efficiency and Trustworthiness using Natural Language Supervision
Traditional strategies to build machine learning based classification systems employ discrete labels as targets. This limits the usefulness of such systems in two ways. First, the generalizability of these systems is limited ... -
Relating Representations in Deep Learning and the Brain
Deep Neural Networks (DNN) inspired by the human brain have redefined the state-of-the-art performance in AI during the past decade. Much of the research is still trying to understand and explain the function of these ...