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Found 8 papers, 0 papers with code
Toward Transparent Sequence Models with Model-Based Tree Markov Model
In this study, we address the interpretability issue in complex, black-box Machine Learning models applied to sequence data.
Towards Model-informed Precision Dosing with Expert-in-the-loop Machine Learning
Machine Learning (ML) and its applications have been transforming our lives but it is also creating issues related to the development of fair, accountable, transparent, and ethical Artificial Intelligence.
Topic Diffusion Discovery Based on Deep Non-negative Autoencoder
Researchers have been overwhelmed by the explosion of research articles published by various research communities.
Discovering Drug-Drug and Drug-Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forests
Patients with Acute Kidney Injury (AKI) increase mortality, morbidity, and long-term adverse events.
Towards Interpretable Deep Extreme Multi-label Learning
In this paper, we discuss the machine learning interpretability of a real-world application, eXtreme Multi-label Learning (XML), which involves learning models from annotated data with many pre-defined labels.
Process Discovery using Classification Tree Hidden Semi-Markov Model
Our experimental result shows that the proposed approach can answer a kind of question-"what are the most frequent sequence of system dynamics relevant to a given sequence of observable events?".
Topic Diffusion Discovery based on Sparseness-constrained Non-negative Matrix Factorization
Due to recent explosion of text data, researchers have been overwhelmed by ever-increasing volume of articles produced by different research communities.
Process Monitoring Using Maximum Sequence Divergence
In this paper, we consider monitoring temporal system state sequences to help detect the changes of dynamic systems, check the divergence of the system development, and evaluate the significance of the deviation.
