David Radke

According to our database, David Radke authored at least 11 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teams.

David Radke, Kyle Tilbury

CoRR, 2023

Analyzing Passing Metrics in Ice Hockey using Puck and Player Tracking Data.

David Radke, Jaxin Lu, Jackson Woloschuk, Tin Le, Daniel Radke, Charlie Liu, Tim Brecht

Proceedings of the Linköping Hockey Analytics Conference 2023 Research Track, 2023

Towards a Better Understanding of Learning with Multiagent Teams.

David Radke, Kate Larson, Tim Brecht, Kyle Tilbury

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Presenting Multiagent Challenges in Team Sports Analytics.

David Radke, Alexi Orchard

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

The Importance of Credo in Multiagent Learning.

David Radke, Kate Larson, Tim Brecht

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

An Analysis of Engineering Students' Responses to an AI Ethics Scenario.

Alexi Orchard, David Radke

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Identifying Completed Pass Types and Improving Passing Lane Models.

David Radke, Tim Brecht, Daniel Radke

Proceedings of the Linköping Hockey Analytics Conference 2022 Research Track, 2022

Exploring the Benefits of Teams in Multiagent Learning.

David Radke, Kate Larson, Tim Brecht

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2020

Beyond Measurement: Extracting Vegetation Height from High Resolution Imagery with Deep Learning.

David Radke, Daniel Radke, John Radke

Remote. Sens., 2020

Can Future Wireless Networks Detect Fires?

David Radke, Omid Abari, Tim Brecht, Kate Larson

Proceedings of the BuildSys '20: The 7th ACM International Conference on Systems for Energy-Efficient Buildings, 2020

2019

FireCast: Leveraging Deep Learning to Predict Wildfire Spread.

David Radke, Anna Hessler, Dan Ellsworth

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019