250 citations · 373 across the 12 of their papers we have counts for
12 papers
FocusLearn: Fully-Interpretable, High-Performance Modular Neural Networks for Time Series
Qiqi Su, Christos Kloukinas, Artur d'Avila Garcez
Multivariate time series have many applications, from healthcare and meteorology to life science. Although deep learning models have shown excellent predictive performance for time…
Predicting recovery following stroke: deep learning, multimodal data and feature selection using explainable AI
Adam White, Margarita Saranti, Artur d'Avila Garcez +3
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…
Continual Reasoning: Non-Monotonic Reasoning in Neurosymbolic AI using Continual Learning
Sofoklis Kyriakopoulos, Artur S. d'Avila Garcez
Despite the extensive investment and impressive recent progress at reasoning by similarity, deep learning continues to struggle with more complex forms of reasoning such as non-mon…
Logical Boltzmann Machines
Son N. Tran, Artur d'Avila Garcez
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…
Accountability in AI: From Principles to Industry-specific Accreditation
Chris Percy, Simo Dragicevic, Sanjoy Sarkar +1
Recent AI-related scandals have shed a spotlight on accountability in AI, with increasing public interest and concern. This paper draws on literature from public policy and governa…
Contrastive Counterfactual Visual Explanations With Overdetermination
Adam White, Kwun Ho Ngan, James Phelan +4
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…
Logic Tensor Networks
Samy Badreddine, Artur d'Avila Garcez, Luciano Serafini +1
Artificial Intelligence agents are required to learn from their surroundings and to reason about the knowledge that has been learned in order to make decisions. While state-of-the-…
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
Luis C. Lamb, Artur Garcez, Marco Gori +3
Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relationa…
Linear-Time Sequence Classification using Restricted Boltzmann Machines
Son N. Tran, Srikanth Cherla, Artur Garcez +1
Classification of sequence data is the topic of interest for dynamic Bayesian models and Recurrent Neural Networks (RNNs). While the former can explicitly model the temporal depend…
Runtime Verification Through Forward Chaining
Alan Perotti, Guido Boella, Artur d'Avila Garcez
In this paper we present a novel rule-based approach for Runtime Verification of FLTL properties over finite but expanding traces. Our system exploits Horn clauses in implication f…
A Hybrid Recurrent Neural Network For Music Transcription
Siddharth Sigtia, Emmanouil Benetos, Nicolas Boulanger-Lewandowski +3
We investigate the problem of incorporating higher-level symbolic score-like information into Automatic Music Transcription (AMT) systems to improve their performance. We use recur…
Adaptive Feature Ranking for Unsupervised Transfer Learning
Son N. Tran, Artur d'Avila Garcez
Transfer Learning is concerned with the application of knowledge gained from solving a problem to a different but related problem domain. In this paper, we propose a method and eff…