Text-based reinforcement learning agents have predominantly been neural
...
With the growing interest in large language models, the need for evaluat...
Using reinforcement learning for automated theorem proving has recently
...
Nearly all general-purpose neural semantic parsers generate logical form...
Most existing Time series classification (TSC) models lack interpretabil...
Resource Description Framework (RDF) and Property Graph (PG) are the two...
Knowledge bases (KBs) are often incomplete and constantly changing in
pr...
Knowledge Base Question Answering (KBQA) tasks that involve complex reas...
Most existing approaches for Knowledge Base Question Answering (KBQA) fo...
Knowledge Base Question Answering (KBQA) tasks that in-volve complex
rea...
Traditional automated theorem provers have relied on manually tuned
heur...
Knowledge base question answering (KBQA) is an important task in Natural...
Automated theorem proving in first-order logic is an active research are...
Recent advances in the integration of deep learning with automated theor...
In this paper, we introduce the problem of knowledge graph contextualiza...
Traditional first-order logic (FOL) reasoning systems usually rely on ma...
Textual entailment is a fundamental task in natural language processing....
Machine learning systems regularly deal with structured data in real-wor...
Open-domain question answering (QA) is an important problem in AI and NL...
Natural Language Inference (NLI) is fundamental to many Natural Language...
A major challenge in designing neural network (NN) systems is to determi...