arXiv:2410.12035v2 Announce Type: replace-cross Abstract: Several variational bounds involving importance weighting ideas generalize the Evidence Lower BOund (ELBO) for marginal likelihood optimization, such as the Importance-weighted Auto-Encoder (IWAE), Variational R\'enyi (VR) and VR-IWAE bounds. Yet, it remains unclear how the joint choice of bound and gradient estimator impacts the behavior of the resulting variational inference (VI) algorithms. This paper provides a unified theoretical comparison of reparameterized (REP) and doubly-reparameterized (DREP) gradient estimators tied to the I
This paper clarifies current developments in variational inference, a core technique in machine learning, offering a unified theoretical comparison of gradient estimators for key bounds.
Improved variational inference methods lead to more efficient and accurate probabilistic models, which are foundational for many advanced AI applications across various industries.
A clearer understanding of how different bounds and gradient estimators impact VI algorithms could lead to more optimized and robust machine learning models.
- · AI researchers
- · Machine learning developers
- · Industries relying on probabilistic AI models
- · Developers of less efficient VI methods
More robust and computationally efficient AI models are developed using optimized variational inference techniques.
Improved AI model performance could accelerate progress in fields like drug discovery, autonomous systems, and natural language processing.
The widespread adoption of more reliable AI could lead to new product categories and increased automation across diverse sectors, impacting global productivity.
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Read at arXiv cs.LG