Year: 2015
Type: preprint
Abstract: Black box variational inference allows researchers to easily prototype and evaluate an array of models. Recent advances allow such algorithms to scale to high dimensions. However, a central question remains: How to specify an expressive variational distribution that maintains efficient computation? To address this, we develop hierarchical variational models (HVMs). HVMs augment a variational approximation with a prior on its parameters, which allows it to capture complex structure for both discrete and continuous latent variables. The algorithm we develop is black box, can be used for any HVM, and has the same computational efficiency as the original approximation. We study HVMs on a variety of deep discrete latent variable models. HVMs generalize other expressive variational distributions and maintains higher fidelity to the posterior.
Source: arXiv (Cornell University)
Institutions: Princeton University, Columbia University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1037014486, G352667211, G4353487559, G4713059963, CAREER: New Directions in Probabilistic Topic Models, G5173174905, G6224274980, G6522282234, Text, Neuroimaging, and Memory: Unified Models of Corpora and Cognition, G6888752980, G693325912, BIGDATA: Mid-Scale: ESCE: Collaborative Research: Discovery and Social Analytics for Large-Scale Scientific Literature, G8876996369
OpenAlex ID: W2228605760