arxiv:2212.01415

Measuring Competency of Machine Learning Systems and Enforcing Reliability

Published on Dec 2, 2022

Authors:

Abstract

Convolutional neural networks leveraging visual imagery for obstacle avoidance in simulated self-driving vehicles can adapt their strategies based on environmental condition representations to maintain performance reliability.

Generated by Qwen/Qwen2.5-Coder-32B-Instruct

We explore the impact of environmental conditions on the competency of machine learning agents and how real-time competency assessments improve the reliability of ML agents. We learn a representation of conditions which impact the strategies and performance of the ML agent enabling determination of actions the agent can make to maintain operator expectations in the case of a convolutional neural network that leverages visual imagery to aid in the obstacle avoidance task of a simulated self-driving vehicle.

View arXiv page View PDF Add to collection

Community

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment

Upvote

Get this paper in your agent:

hf papers read 2212.01415

Don't have the latest CLI?

curl -LsSf https://hf.co/cli/install.sh | bash

Models citing this paper 0

No model linking this paper

Cite arxiv.org/abs/2212.01415 in a model README.md to link it from this page.

Datasets citing this paper 1

Spaces citing this paper 0

No Space linking this paper

Cite arxiv.org/abs/2212.01415 in a Space README.md to link it from this page.

Collections including this paper 0

No Collection including this paper

Add this paper to a collection to link it from this page.