Zobrazeno 1 - 10
of 228
pro vyhledávání: '"Artur Avila"'
Multivariate time series have many applications, from healthcare and meteorology to life science. Although deep learning models have shown excellent predictive performance for time series, they have been criticised for being "black-boxes" or non-inte
Externí odkaz:
http://arxiv.org/abs/2311.16834
Autor:
White, Adam, Saranti, Margarita, Garcez, Artur d'Avila, Hope, Thomas M. H., Price, Cathy J., Bowman, Howard
Machine learning offers great potential for automated prediction of post-stroke symptoms and their response to rehabilitation. Major challenges for this endeavour include the very high dimensionality of neuroimaging data, the relatively small size of
Externí odkaz:
http://arxiv.org/abs/2310.19174
Autor:
Pontelli, Enrico, Costantini, Stefania, Dodaro, Carmine, Gaggl, Sarah, Calegari, Roberta, Garcez, Artur D'Avila, Fabiano, Francesco, Mileo, Alessandra, Russo, Alessandra, Toni, Francesca
Publikováno v:
EPTCS 385, 2023
This volume contains the Technical Communications presented at the 39th International Conference on Logic Programming (ICLP 2023), held at Imperial College London, UK from July 9 to July 15, 2023. Technical Communications included here concern the Ma
Externí odkaz:
http://arxiv.org/abs/2308.14898
Autor:
Odense, Simon, Garcez, Artur d'Avila
The field of neuro-symbolic AI aims to benefit from the combination of neural networks and symbolic systems. A cornerstone of the field is the translation or encoding of symbolic knowledge into neural networks. Although many neuro-symbolic methods an
Externí odkaz:
http://arxiv.org/abs/2212.12050
Publikováno v:
1st Workshop on Human and Machine Decisions (WHMD 2021), NeurIPS 2021
We propose neural-symbolic integration for abstract concept explanation and interactive learning. Neural-symbolic integration and explanation allow users and domain-experts to learn about the data-driven decision making process of large neural models
Externí odkaz:
http://arxiv.org/abs/2112.11805
Autor:
Tran, Son N., Garcez, Artur d'Avila
The idea of representing symbolic knowledge in connectionist systems has been a long-standing endeavour which has attracted much attention recently with the objective of combining machine learning and scalable sound reasoning. Early work has shown a
Externí odkaz:
http://arxiv.org/abs/2112.05841
Autor:
Bennetot, Adrien, Donadello, Ivan, Qadi, Ayoub El, Dragoni, Mauro, Frossard, Thomas, Wagner, Benedikt, Saranti, Anna, Tulli, Silvia, Trocan, Maria, Chatila, Raja, Holzinger, Andreas, Garcez, Artur d'Avila, Díaz-Rodríguez, Natalia
Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although they have great generalization and prediction skills, their functioning does not allow obtaining detailed exp
Externí odkaz:
http://arxiv.org/abs/2111.14260
Detecting money laundering in gambling is becoming increasingly challenging for the gambling industry as consumers migrate to online channels. Whilst increasingly stringent regulations have been applied over the years to prevent money laundering in g
Externí odkaz:
http://arxiv.org/abs/2109.12546
Autor:
White, Adam, Garcez, Artur d'Avila
In many applications, it is important to be able to explain the decisions of machine learning systems. An increasingly popular approach has been to seek to provide \emph{counterfactual instance explanations}. These specify close possible worlds in wh
Externí odkaz:
http://arxiv.org/abs/2109.09809
Autor:
White, Adam, Ngan, Kwun Ho, Phelan, James, Afgeh, Saman Sadeghi, Ryan, Kevin, Reyes-Aldasoro, Constantino Carlos, Garcez, Artur d'Avila
A novel explainable AI method called CLEAR Image is introduced in this paper. CLEAR Image is based on the view that a satisfactory explanation should be contrastive, counterfactual and measurable. CLEAR Image explains an image's classification probab
Externí odkaz:
http://arxiv.org/abs/2106.14556