Ms. Mandy Sack Profile

Mandy Sack

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 30 May 2022 Presentation + Paper

A data-centric reinforcement learning approach for self-updating machine learning models

Mandy Sack

Proceedings Volume 12119, 121190H (2022) https://doi.org/10.1117/12.2615873

KEYWORDS: Data modeling, Performance modeling, Process modeling, Systems modeling, Machine learning, Image classification, Silver, Sensors, Optimization (mathematics)

Read Abstract +

Continuously Updating Reinforcement Learning (CURL) demonstrates the ability to rapidly maintain deployed ML models when there is a change in use case such as a denied target with minimal performance effects. Traditional Machine Learning (ML) lifecycle requires ML models to be retrained and redeployed in order to maintain performance of deployed models experiencing changes in underlying data such as data drift. Data drift can include a wide variety of changes in data such as the addition of a new class, operating in an entirely new environment, mislabeled data, or subtle changes in targets over time. (CURL) deviates from this traditional lifecycle with dynamic updates using Reinforcement Learning (RL) to identify and capture data changes, and then automatically retrain the model with data changes. CURL learns to identify changes in data through its RL policy that is designed to maximize the reward for identifying changes in data. Specifically, CURL’s RL approach includes an environment with both the model’s performance and current prediction confidence as the observation space for the agent to act on the discrete action space, and reward function of the model’s accuracy subtracted by the labeling cost to learn data changes. Our controlled experiment demonstrated that the same distribution of denied target data (3%) was found by our RL policy, and our retrained model exceeded the initial classifier performance. CURL can be considered a general purpose technology that could be applied to a wide spectrum of fielded ML systems.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years