no code implementations • 15 Feb 2022 • Abdul Karim Obeid, Peter Bruza, Catarina Moreira, Axel Bruns, Daniel Angus
Through this formalisation, we extend the combinatorial approach to support a measurement and treatment of disturbance, and offer techniques to separately distinguish noise and causal influences.
no code implementations • 21 Mar 2021 • Dongsheng Wang, Prayag Tiwari, Sahil Garg, Hongyin Zhu, Peter Bruza
In this paper, we propose a novel lightweight relation extraction approach of structural block driven - convolutional neural learning.
no code implementations • 7 Mar 2021 • Yu-Liang Chou, Catarina Moreira, Peter Bruza, Chun Ouyang, Joaquim Jorge
This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence.
no code implementations • 21 Jul 2020 • Catarina Moreira, Yu-Liang Chou, Mythreyi Velmurugan, Chun Ouyang, Renuka Sindhgatta, Peter Bruza
This has led to an increased interest in interpretable machine learning, where post hoc interpretation presents a useful mechanism for generating interpretations of complex learning models.
no code implementations • 15 Jul 2020 • Prayag Tiwari, Shahram Dehdashti, Abdul Karim Obeid, Massimo Melucci, Peter Bruza
In this paper, by mapping datasets to a set of non-linear coherent states, the process of encoding inputs in quantum states as a non-linear feature map is re-interpreted.
no code implementations • 2 Jun 2020 • Shahram Dehdashti, Catarina Moreira, Abdul Karim Obeid, Peter Bruza
This paper uses deformed coherent states, based on a deformed Weyl-Heisenberg algebra that unifies the well-known SU(2), Weyl-Heisenberg, and SU(1, 1) groups, through a common parameter.
no code implementations • 30 May 2020 • Catarina Moreira, Matheus Hammes, Rasim Serdar Kurdoglu, Peter Bruza
This paper provides the foundations of a unified cognitive decision-making framework (QulBIT) which is derived from quantum theory.
no code implementations • 21 Feb 2020 • Catarina Moreira, Renuka Sindhgatta, Chun Ouyang, Peter Bruza, Andreas Wichert
We see certain distinct features used for predictions that provide useful insights about the type of cancer, along with features that do not generalize well.
Decision Making Interpretability Techniques for Deep Learning
no code implementations • 20 Jan 2020 • Sagar Uprety, Prayag Tiwari, Shahram Dehdashti, Lauren Fell, Dawei Song, Peter Bruza, Massimo Melucci
A large number of studies in cognitive science have revealed that probabilistic outcomes of certain human decisions do not agree with the axioms of classical probability theory.
no code implementations • 25 Jul 2019 • Sagar Uprety, Shahram Dehdashti, Lauren Fell, Peter Bruza, Dawei Song
We study the interaction between the relevance dimensions using the mathematical framework of Quantum Theory.
no code implementations • 11 May 2019 • Catarina Moreira, Lauren Fell, Shahram Dehdashti, Peter Bruza, Andreas Wichert
We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information than classical models.
1 code implementation • ALTA 2018 • Lance De Vine, Shlomo Geva, Peter Bruza
It has been demonstrated that vector-based representations of words trained on large text corpora encode linguistic regularities that may be exploited via the use of vector space arithmetic.