3 citations · 3 across the 4 of their papers we have counts for
4 papers
Tunable Quantum Neural Networks in the QPAC-Learning Framework
Viet Pham Ngoc, David Tuckey, Herbert Wiklicky
In this paper, we investigate the performances of tunable quantum neural networks in the Quantum Probably Approximately Correct (QPAC) learning framework. Tunable neural networks a…
PASOCS: A Parallel Approximate Solver for Probabilistic Logic Programs under the Credal Semantics
David Tuckey, Alessandra Russo, Krysia Broda
The Credal semantics is a probabilistic extension of the answer set semantics which can be applied to programs that may or may not be stratified. It assigns to atoms a set of accep…
A general framework for scientifically inspired explanations in AI
David Tuckey, Alessandra Russo, Krysia Broda
Explainability in AI is gaining attention in the computer science community in response to the increasing success of deep learning and the important need of justifying how such sys…
Saliency Maps Generation for Automatic Text Summarization
David Tuckey, Krysia Broda, Alessandra Russo
Saliency map generation techniques are at the forefront of explainable AI literature for a broad range of machine learning applications. Our goal is to question the limits of these…