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Tabular Diffusion in Practice: SDEdit and Conditioning
Two techniques for steering a trained diffusion model toward specific outputs without retraining it.
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Tabular Diffusion in Practice: Variance Schedules, Time Embeddings, and EMA
A closer look at the implementation choices that matter when training a diffusion model on tabular data.
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Diffusion Models for Tabular Data
Why tabular data is a hard target for generative models, and how diffusion models are being adapted to handle it.
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Building Intuition for Persistent Homology
An interactive introduction to persistent homology — what it measures, how a filtration works, and what persistence diagrams are actually telling you.
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TDA and Machine Learning
A high-level, no code introduction to how topological data analysis and machine learning complement each other.