Deciphering the Link Between Information and Interpretability in Deep Learning and Artificial Intelligence
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 31 May 2025 | Viewed by 1005
Special Issue Editors
Interests: statistical physics; mathematical biology; information theory; network science; dynamical systems
Special Issue Information
Dear Colleagues,
Deep learning models have emerged as a way to reliably identify patterns and correlations in large, complex datasets, but the application of these models to increasingly complex tasks, such as spatiotemporal object tracking or synthetic data generation, has necessitated increasingly sophisticated model architectures whose emergent capabilities are often difficult to understand and interpret from a first principles perspective. Information theory, with its focus on quantifying correlations and uncertainty within datasets, is fertile ground for novel investigations into the ways in which the structure and parameters of a deep learning network synergize with each other to extract predictive patterns within a dataset. In this Special Issue, we are seeking manuscripts that leverage information theory or other statistical or correlative metrics to interrogate how the robust and multifaceted functionality of deep learning architectures or artificial intelligence emerges from the iteration of relatively straightforward mathematical operations that are individually agnostic to the particulars of the training data. As society considers artificial intelligence algorithms a replacement for human productivity, there remains an unacceptable level of mystery involving the mechanisms of their remarkable predictive and even creative capabilities. It is our hope that this Special Issue might help to bridge this gap and bring the state of science more in line with the current state of the art.
Dr. Michael L. Mayo
Dr. Kevin R. Pilkiewicz
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- neural networks
- interpretability
- statistical analysis
- information theory
- feature analysis
- object detection/tracking
- generative artificial intelligence
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