3 citations · 5 across the 10 of their papers we have counts for
10 papers
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…
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…
On Faster Marginalization with Squared Circuits via Orthonormalization
Lorenzo Loconte, Antonio Vergari
Squared tensor networks (TNs) and their generalization as parameterized computational graphs -- squared circuits -- have been recently used as expressive distribution estimators in…
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…
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…
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…
Subtractive Mixture Models via Squaring: Representation and Learning
Lorenzo Loconte, Aleksanteri M. Sladek, Stefan Mengel +4
Mixture models are traditionally represented and learned by adding several distributions as components. Allowing mixtures to subtract probability mass or density can drastically re…
How to Turn Your Knowledge Graph Embeddings into Generative Models
Lorenzo Loconte, Nicola Di Mauro, Robert Peharz +1
Some of the most successful knowledge graph embedding (KGE) models for link prediction -- CP, RESCAL, TuckER, ComplEx -- can be interpreted as energy-based models. Under this persp…
DeeProb-kit: a Python Library for Deep Probabilistic Modelling
Lorenzo Loconte, Gennaro Gala
DeeProb-kit is a unified library written in Python consisting of a collection of deep probabilistic models (DPMs) that are tractable and exact representations for the modelled prob…