# Unveil the unseen: Exploit information hidden in noise

arXiv: https://arxiv.org/abs/2209.08376 · HTML: https://mlsanity.com/paper/2209.08376/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 71 references that are in the map.
- First posted on arXiv 17 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Less is more: sampling chemical space with active learning](https://mlsanity.com/paper/1801.09319/) (2018)
- [Anomaly Detection using Autoencoders in High Performance Computing Systems](https://mlsanity.com/paper/1811.05269/) (2018)
- [Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data](https://mlsanity.com/paper/2007.08361/) (2020)
- [Design of a nickel-base superalloy using a neural network](https://mlsanity.com/paper/1803.03039/) (2018)
- [Probabilistic design of a molybdenum-base alloy using a neural network](https://mlsanity.com/paper/1803.00879/) (2018)

## Built on by (most cited first, 1 of 1)

- [Probabilistic selection and design of concrete using machine learning](https://mlsanity.com/paper/2304.11226/) (2023)

## Abstract (arXiv)

Noise and uncertainty are usually the enemy of machine learning, noise in training data leads to uncertainty and inaccuracy in the predictions. However, we develop a machine learning architecture that extracts crucial information out of the noise itself to improve the predictions. The phenomenology computes and then utilizes uncertainty in one target variable to predict a second target variable. We apply this formalism to PbZr0.7Sn0.3O3 crystal, using the uncertainty in dielectric constant to extrapolate heat capacity, correctly predicting a phase transition that otherwise cannot be extrapolated. For the second example -- single-particle diffraction of droplets -- we utilize the particle count together with its uncertainty to extrapolate the ground truth diffraction amplitude, delivering better predictions than when we utilize only the particle count. Our generic formalism enables the exploitation of uncertainty in machine learning, which has a broad range of applications in the physical sciences and beyond.