7 papers
Fast and Expressive Multi-Byte Prediction with Probabilistic Circuits
Andreas Grivas, Lorenzo Loconte, Emile van Krieken +6
Multi-token prediction (MTP) is a prominent strategy to significantly speed up generation in large language models (LLMs), especially in byte-level LLMs, which are tokeniser-free b…
Better Later Than Sooner: Neuro-Symbolic Knowledge Graph Construction via Ontology-grounded Post-extraction Correction
Lorenzo Loconte, Timothy Hospedales, Cristina Cornelio
Question answering (QA) is a core challenge in AI, particularly for complex queries requiring multi-hop reasoning across documents, or symbolic operations like aggregation or exhau…
How to Square Tensor Networks and Circuits Without Squaring Them
Lorenzo Loconte, Adrián Javaloy, Antonio Vergari
Squared tensor networks (TNs) and their extension as computational graphs--squared circuits--have been used as expressive distribution estimators, yet supporting closed-form margin…
Is Complex Query Answering Really Complex?
Cosimo Gregucci, Bo Xiong, Daniel Hernandez +4
Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current benchmarks for CQA might not be…
Sum of Squares Circuits
Lorenzo Loconte, Stefan Mengel, Antonio Vergari
Designing expressive generative models that support exact and efficient inference is a core question in probabilistic ML. Probabilistic circuits (PCs) offer a framework where this…
What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)?
Lorenzo Loconte, Antonio Mari, Gennaro Gala +5
This paper establishes a rigorous connection between circuit representations and tensor factorizations, two seemingly distinct yet fundamentally related areas. By connecting these…