# Improving Prediction Confidence in Learning-Enabled Autonomous Systems

arXiv: https://arxiv.org/abs/2110.03123 · HTML: https://mlsanity.com/paper/2110.03123/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 7 Oct 2021.

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

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning](https://mlsanity.com/paper/1803.04765/) (2018)
- [Improved Embeddings with Easy Positive Triplet Mining](https://mlsanity.com/paper/1904.04370/) (2019)
- [Trusted Confidence Bounds for Learning Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2003.05107/) (2020)
- [Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components](https://mlsanity.com/paper/2001.05014/) (2020)

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

- [Confidence Composition for Monitors of Verification Assumptions](https://mlsanity.com/paper/2111.03782/) (2021)

## Abstract (arXiv)

Autonomous systems use extensively learning-enabled components such as deep neural networks (DNNs) for prediction and decision making. In this paper, we utilize a feedback loop between learning-enabled components used for classification and the sensors of an autonomous system in order to improve the confidence of the predictions. We design a classifier using Inductive Conformal Prediction (ICP) based on a triplet network architecture in order to learn representations that can be used to quantify the similarity between test and training examples. The method allows computing confident set predictions with an error rate predefined using a selected significance level. A feedback loop that queries the sensors for a new input is used to further refine the predictions and increase the classification accuracy. The method is computationally efficient, scalable to high-dimensional inputs, and can be executed in a feedback loop with the system in real-time. The approach is evaluated using a traffic sign recognition dataset and the results show that the error rate is reduced.