Approximate Posterior Inference For Latent Variable Discovery

via “scalable-posterior-inference-via-variational-approximation”

* 🏆 2006: [Reducing the Dimensionality of Data with Neural Networks (Autoencoder)](https://www.science.org/doi/abs/10.1126/science.1127647)

Unique: Introduces mean-field variational inference to topic modeling (Blei et al. 2003), replacing expensive Gibbs sampling with coordinate ascent optimization over variational parameters — enabling orders-of-magnitude speedup while maintaining interpretability through explicit posterior approximation

Dramatically faster than Gibbs sampling on large corpora (hours vs days) while providing explicit uncertainty estimates unlike deterministic LSA; trades some accuracy for scalability but remains more principled than heuristic approximations