Zobrazeno 1 - 10
of 158
pro vyhledávání: '"De Campos, Cassio P."'
Autor:
Loconte, Lorenzo, Mari, Antonio, Gala, Gennaro, Peharz, Robert, de Campos, Cassio, Quaeghebeur, Erik, Vessio, Gennaro, Vergari, Antonio
This paper establishes a rigorous connection between circuit representations and tensor factorizations, two seemingly distinct yet fundamentally related areas. By connecting these fields, we highlight a series of opportunities that can benefit both c
Externí odkaz:
http://arxiv.org/abs/2409.07953
We introduce Nerva, a fast neural network library under development in C++. It supports sparsity by using the sparse matrix operations of Intel's Math Kernel Library (MKL), which eliminates the need for binary masks. We show that Nerva significantly
Externí odkaz:
http://arxiv.org/abs/2407.17437
Probabilistic integral circuits (PICs) have been recently introduced as probabilistic models enjoying the key ingredient behind expressive generative models: continuous latent variables (LVs). PICs are symbolic computational graphs defining continuou
Externí odkaz:
http://arxiv.org/abs/2406.06494
Probabilistic Circuits (PCs) are prominent tractable probabilistic models, allowing for a range of exact inferences. This paper focuses on the main algorithm for training PCs, LearnSPN, a gold standard due to its efficiency, performance, and ease of
Externí odkaz:
http://arxiv.org/abs/2403.14504
This work addresses integrating probabilistic propositional logic constraints into the distribution encoded by a probabilistic circuit (PC). PCs are a class of tractable models that allow efficient computations (such as conditional and marginal proba
Externí odkaz:
http://arxiv.org/abs/2403.13125
Continuous latent variables (LVs) are a key ingredient of many generative models, as they allow modelling expressive mixtures with an uncountable number of components. In contrast, probabilistic circuits (PCs) are hierarchical discrete mixtures repre
Externí odkaz:
http://arxiv.org/abs/2310.16986
Multi-dimensional classification (MDC) can be employed in a range of applications where one needs to predict multiple class variables for each given instance. Many existing MDC methods suffer from at least one of inaccuracy, scalability, limited use
Externí odkaz:
http://arxiv.org/abs/2306.06517
Probabilistic models based on continuous latent spaces, such as variational autoencoders, can be understood as uncountable mixture models where components depend continuously on the latent code. They have proven to be expressive tools for generative
Externí odkaz:
http://arxiv.org/abs/2209.10584
Autor:
Benavoli, Alessio, de Campos, Cassio
A fundamental task in AI is to assess (in)dependence between mixed-type variables (text, image, sound). We propose a Bayesian kernelised correlation test of (in)dependence using a Dirichlet process model. The new measure of (in)dependence allows us t
Externí odkaz:
http://arxiv.org/abs/2105.04001
Decision Trees and Random Forests are among the most widely used machine learning models, and often achieve state-of-the-art performance in tabular, domain-agnostic datasets. Nonetheless, being primarily discriminative models they lack principled met
Externí odkaz:
http://arxiv.org/abs/2007.05721