Exploring Transparency of Models
My research investigates methods to enhance the interpretability and explainability of complex AI systems, developing techniques that make the inner workings of machine learning models more transparent and understandable.
Revealing Hidden Layers
My Writings on Model Transparency
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Relevant Research & Technical Articles
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Key Concepts in Model Transparency
Interpretability
Developing techniques to explain how AI models make decisions in a way that is comprehensible to human observers.
Explainable AI (XAI)
Approaches and methodologies that make the reasoning and decision-making processes of AI systems more transparent.
Model Visualization
Techniques for visually representing the internal workings and learned representations of neural networks.
Uncertainty Quantification
Methods for measuring and communicating the confidence and potential limitations of AI model predictions.