York, Sokhna, Ali, Alina, Lashbrooke, David, Yepez-Lopez, Rodrigo, Barajas, Carlos, Gobbert, Matthias, and Polf, Jerimy. Promising Hyperparameter Configurations for Deep Fully Connected Neural Networks to Improve Image Reconstruction in Proton Radiotherapy. Retrieved from https://par.nsf.gov/biblio/10313428. 2021 National Symposium for NSF REU Research in Data Science, Systems, and Security (REU 2021 Symposium) .

York, Sokhna, Ali, Alina, Lashbrooke, David, Yepez-Lopez, Rodrigo, Barajas, Carlos, Gobbert, Matthias, & Polf, Jerimy. Promising Hyperparameter Configurations for Deep Fully Connected Neural Networks to Improve Image Reconstruction in Proton Radiotherapy. 2021 National Symposium for NSF REU Research in Data Science, Systems, and Security (REU 2021 Symposium), (). Retrieved from https://par.nsf.gov/biblio/10313428.

York, Sokhna, Ali, Alina, Lashbrooke, David, Yepez-Lopez, Rodrigo, Barajas, Carlos, Gobbert, Matthias, and Polf, Jerimy. "Promising Hyperparameter Configurations for Deep Fully Connected Neural Networks to Improve Image Reconstruction in Proton Radiotherapy". 2021 National Symposium for NSF REU Research in Data Science, Systems, and Security (REU 2021 Symposium) (). Country unknown/Code not available. https://par.nsf.gov/biblio/10313428.

@article{osti_10313428,
place = {Country unknown/Code not available}, title = {Promising Hyperparameter Configurations for Deep Fully Connected Neural Networks to Improve Image Reconstruction in Proton Radiotherapy}, url = {https://par.nsf.gov/biblio/10313428}, abstractNote = {}, journal = {2021 National Symposium for NSF REU Research in Data Science, Systems, and Security (REU 2021 Symposium)}, author = {York, Sokhna and Ali, Alina and Lashbrooke, David and Yepez-Lopez, Rodrigo and Barajas, Carlos and Gobbert, Matthias and Polf, Jerimy}, }