activity
20172022
most citedSPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks

32 citations · 80 across the 11 of their papers we have counts for

collaborators

18 papers

cs.CL20242 cited

Logically Consistent Language Models via Neuro-Symbolic Integration

Diego Calanzone, Stefano Teso, Antonio Vergari

Large language models (LLMs) are a promising venue for natural language understanding and generation. However, current LLMs are far from reliable: they are prone to generating non-…

cs.LG2022

ChemAlgebra: Algebraic Reasoning on Chemical Reactions

Andrea Valenti, Davide Bacciu, Antonio Vergari

While showing impressive performance on various kinds of learning tasks, it is yet unclear whether deep learning models have the ability to robustly tackle reasoning tasks. than by…

cs.LG2022

Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs

Stefano Teso, Antonio Vergari

In this position paper, we study interactive learning for structured output spaces, with a focus on active learning, in which labels are unknown and must be acquired, and on skepti…

stat.ML20213 cited

A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries

Antonio Vergari, YooJung Choi, Anji Liu +2

Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models. In this paper, we show how complex inference scen…

cs.LG2021

Tractable Computation of Expected Kernels

Wenzhe Li, Zhe Zeng, Antonio Vergari +1

Computing the expectation of kernel functions is a ubiquitous task in machine learning, with applications from classical support vector machines to exploiting kernel embeddings of…

cs.CL2021

An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games

Alessandro Suglia, Yonatan Bisk, Ioannis Konstas +4

Guessing games are a prototypical instance of the "learning by interacting" paradigm. This work investigates how well an artificial agent can benefit from playing guessing games wh…