Search Results for author: Renuka Sindhgatta

Found 11 papers, 4 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.

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

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

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

Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System

no code implementations NAACL 2019 Shazia Afzal, Tejas Dhamecha, Nirmal Mukhi, Renuka Sindhgatta, Smit Marvaniya, Matthew Ventura, Jessica Yarbro

There are significant challenges involved in the design and implementation of a dialog-based tutoring system (DBT) ranging from domain engineering to natural language classification and eventually instantiating an adaptive, personalized dialog strategy.

General Classification

Joint Multi-Domain Learning for Automatic Short Answer Grading

no code implementations25 Feb 2019 Swarnadeep Saha, Tejas I. Dhamecha, Smit Marvaniya, Peter Foltz, Renuka Sindhgatta, Bikram Sengupta

On a large-scale industry dataset and a benchmarking dataset, we show that our model performs significantly better than existing techniques which either learn domain-specific models or adapt a generic similarity scoring model from a large corpus.

Domain Adaptation

DeepProcess: Supporting business process execution using a MANN-based recommender system

1 code implementation3 Feb 2018 Asjad Khan, Hung Le, Kien Do, Truyen Tran, Aditya Ghose, Hoa Dam, Renuka Sindhgatta

Process-aware Recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions to take next.

Activity Prediction Recommendation Systems

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