Differentiable filtering framework learns Hidden Markov Model parameters efficiently

Researchers have developed a new framework called Belief Net for learning Hidden Markov Models (HMMs). This approach uses a differentiable filtering process, treating the forward filter as a structured neural network optimized via stochastic gradient descent. Belief Net offers improved convergence over traditional methods like Baum-Welch and can recover parameters in settings where spectral algorithms fail, while maintaining interpretability by directly learning HMM parameters. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Introduces a novel, interpretable neural network approach for learning sequential data models, potentially improving performance and convergence over existing methods.

RANK_REASON This is a research paper introducing a new framework for learning Hidden Markov Models.

Read on arXiv cs.LG →

• Belief Net
• Hidden Markov Models
• Baum-Welch algorithm
• Heng-Sheng Chang

• paper
• other