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Core Idea of Deep Probabilistic Models
- Deep Probabilistic Graphical Modeling blends probabilistic graphical models with deep learning to gain interpretability and uncertainty quantification.
- It uses neural networks within a generative process framework to add flexibility in model specification and inference.
Story of Topic RNN Creation
- The idea for Topic RNN came from an internship where recurrent neural networks and topic models were combined.
- It captures both local sequential dependencies with RNNs and global thematic context with latent topics.
Value of Inductive Biases
- Inductive biases in models allow faster training and control by encoding known structure explicitly.
- Controllability in probabilistic models arises from encoding known dependencies, like patient-specific global latent variables in medical data.