# Non-Volatile Memory Accelerated Posterior Estimation

arXiv: https://arxiv.org/abs/2202.10522 · HTML: https://mlsanity.com/paper/2202.10522/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [A Simple Baseline for Bayesian Uncertainty in Deep Learning](https://mlsanity.com/paper/1902.02476/) (2019)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [Stochastic Gradient Hamiltonian Monte Carlo](https://mlsanity.com/paper/1402.4102/) (2014)
- [Basic Performance Measurements of the Intel Optane DC Persistent Memory Module](https://mlsanity.com/paper/1903.05714/) (2019)
- [An Architecture for Memory Centric Active Storage (MCAS)](https://mlsanity.com/paper/2103.00007/) (2021)

## Abstract (arXiv)

Bayesian inference allows machine learning models to express uncertainty. Current machine learning models use only a single learnable parameter combination when making predictions, and as a result are highly overconfident when their predictions are wrong. To use more learnable parameter combinations efficiently, these samples must be drawn from the posterior distribution. Unfortunately computing the posterior directly is infeasible, so often researchers approximate it with a well known distribution such as a Gaussian. In this paper, we show that through the use of high-capacity persistent storage, models whose posterior distribution was too big to approximate are now feasible, leading to improved predictions in downstream tasks.