Mark Hobbs

According to our database, Mark Hobbs authored at least 9 papers between 2020 and 2024.

Collaborative distances:

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

2024

Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids.

Saurabh Deshpande, Hussein Rappel, Mark Hobbs, Stéphane P. A. Bordas, Jakub Lengiewicz

CoRR, 2024

Predicting Major Donor Prospects Using Machine Learning.

Greg Lee, Aishwarya Vaishali Sathyamurthi, Mark Hobbs

Proceedings of the 16th International Conference on Agents and Artificial Intelligence, 2024

2023

Effects of Feature Types on Donor Journey.

Greg Lee, Ajith Kumar Veera Raghavan, Mark Hobbs

Proceedings of the Agents and Artificial Intelligence - 15th International Conference, 2023

Adding Time and Subject Line Features to the Donor Journey.

Greg Lee, Ajith Kumar Veera Raghavan, Mark Hobbs

Proceedings of the 15th International Conference on Agents and Artificial Intelligence, 2023

2022

A probabilistic peridynamic framework with an application to the study of the statistical size effect.

Mark Hobbs, Hussein Rappel, Tim J. Dodwell

CoRR, 2022

Optimizing the Feature Set for Machine Learning Charitable Predictions.

Greg Lee, Jordan Pippy, Mark Hobbs

Proceedings of the AI 2022: Advances in Artificial Intelligence, 2022

2021

PeriPy - A High Performance OpenCL Peridynamics Package.

B. Boys, Tim J. Dodwell, Mark Hobbs, Mark Girolami

CoRR, 2021

2020

Improving the Donor Journey with Convolutional and Recurrent Neural Networks.

Greg Lee, Ajith Kumar Veera Raghavan, Mark Hobbs

Proceedings of the 19th IEEE International Conference on Machine Learning and Applications, 2020

Machine Learning the Donor Journey.

Greg Lee, Ajith Kumar Veera Raghavan, Mark Hobbs

Proceedings of the Advances in Artificial Intelligence, 2020