Search Results for author: Catarina Moreira

Found 24 papers, 2 papers with code

Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms

no code implementations3 Sep 2021 Bemali Wickramanayake, Zhipeng He, Chun Ouyang, Catarina Moreira, Yue Xu, Renuka Sindhgatta

In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction.

Interpreting Process Predictions using a Milestone-Aware Counterfactual Approach

no code implementations19 Jul 2021 Chihcheng Hsieh, Catarina Moreira, Chun Ouyang

Predictive process analytics often apply machine learning to predict the future states of a running business process.

Explainable AI Enabled Inspection of Business Process Prediction Models

no code implementations16 Jul 2021 Chun Ouyang, Renuka Sindhgatta, Catarina Moreira

As an important branch of state-of-the-art data analytics, business process predictions are also faced with a challenge in regard to the lack of explanation to the reasoning and decision by the underlying `black-box' prediction models.

Decision Making Interpretable Machine Learning

Developing a Fidelity Evaluation Approach for Interpretable Machine Learning

1 code implementation16 Jun 2021 Mythreyi Velmurugan, Chun Ouyang, Catarina Moreira, Renuka Sindhgatta

Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex, and lack transparency.

Explanation Fidelity Evaluation Interpretable Machine Learning

Order Effects in Bayesian Updates

no code implementations16 May 2021 Catarina Moreira, Jose Acacio de Barros

Order effects occur when judgments about a hypothesis's probability given a sequence of information do not equal the probability of the same hypothesis when the information is reversed.

Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications

no code implementations7 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.

Explainable artificial intelligence

An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models

no code implementations21 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.

Decision Making Interpretable Machine Learning

Construction of 'Support Vector' Machine Feature Spaces via Deformed Weyl-Heisenberg Algebra

no code implementations2 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.

QuLBIT: Quantum-Like Bayesian Inference Technologies for Cognition and Decision

no code implementations30 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.

Bayesian Inference Decision Making

An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics

no code implementations21 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

Exploring Interpretability for Predictive Process Analytics

no code implementations22 Dec 2019 Renuka Sindhgatta, Chun Ouyang, Catarina Moreira

The explanations allow us to gain an understanding of the underlying reasons for a prediction and highlight scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data to features used by a predictive model.

Decision Making Interpretable Machine Learning

Towards a Quantum-Like Cognitive Architecture for Decision-Making

no code implementations11 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.

Decision Making

Unifying Decision-Making: a Review on Evolutionary Theories on Rationality and Cognitive Biases

no code implementations29 Nov 2018 Catarina Moreira

In this paper, we make a review on the concepts of rationality across several different fields, namely in economics, psychology and evolutionary biology and behavioural ecology.

Decision Making

Introducing Quantum-Like Influence Diagrams for Violations of the Sure Thing Principle

no code implementations16 Jul 2018 Catarina Moreira, Andreas Wichert

The general idea is to take advantage of the quantum interference terms produced in the quantum-like Bayesian Network to influence the probabilities used to compute the expected utility of some action.

Decision Making

The Relation Between Acausality and Interference in Quantum-Like Bayesian Networks

no code implementations26 Aug 2015 Catarina Moreira, Andreas Wichert

We analyse a quantum-like Bayesian Network that puts together cause/effect relationships and semantic similarities between events.

An Experiment on Using Bayesian Networks for Process Mining

no code implementations25 Mar 2015 Catarina Moreira

Process mining is a technique that performs an automatic analysis of business processes from a log of events with the promise of understanding how processes are executed in an organisation.

On Projection Based Operators in Lp space for Exact Similarity Search

no code implementations12 Feb 2015 Andreas Wichert, Catarina Moreira

We investigate exact indexing for high dimensional Lp norms based on the 1-Lipschitz property and projection operators.

Using Rank Aggregation for Expert Search in Academic Digital Libraries

no code implementations21 Jan 2015 Catarina Moreira, Bruno Martins, Pável Calado

The task of expert finding has been getting increasing attention in information retrieval literature.

Information Retrieval

Learning to Rank Academic Experts in the DBLP Dataset

no code implementations21 Jan 2015 Catarina Moreira, Bruno Martins, Pável Calado

More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combing all of the estimators of expertise.

Information Retrieval Learning-To-Rank

Interference Effects in Quantum Belief Networks

no code implementations30 Sep 2014 Catarina Moreira, Andreas Wichert

This means that probabilistic graphical models based on classical probability theory are too limited to fully simulate and explain various aspects of human decision making.

Decision Making

Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy

no code implementations12 Jun 2013 Catarina Moreira, Andreas Wichert

To deal with these conflicts, we applied the Dempster-Shafer theory of evidence combined with Shannon's Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list.

Information Retrieval

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