91勛圖厙

Elhanafy, Marim; Ravva, Srivaths; Solanki, Abhijeet; Hasan, Syed Rafay. ���Towards Machine Learning Based Fingerprinting of Ultrasonic Sensors.��� Conference Proceedings – IEEE SoutheastCon (2025): 1332���1333.��https://doi.org/10.1109/SoutheastCon56624.2025.10971545.�� “Fingerprinting” is a method used to identify devices based on their unique data patterns���kind of like how… Read More

Khan, Aimal; Yang, Hao; Habib, Daniel Roy Sadek; Ali, Danish; Wu, Jie Ying. “Development of a machine learning-based tension measurement method in robotic surgery.” Surgical Endoscopy (2025). https://doi.org/10.1007/s00464-025-11658-9.�� Each year, over 300,000 people in the U.S. undergo colorectal surgery,… Read More

Newlin, Nancy R.; Kim, Michael E.; Kanakaraj, Praitayini; Pechman, Kimberly; Shashikumar, Niranjana; Moore, Elizabeth; Archer, Derek; Hohman, Timothy; Jefferson, Angela; Moyer, Daniel; Landman, Bennett A. “Learning disentangled representations to harmonize connectome network measures.” Journal of Medical Imaging, vol. 12, no. 1, 2025, 14004, https://doi.org/10.1117/1.JMI.12.1.014004… Read More

Dick, J., Nath, S., Peridis, C., Benjamin, E., Kolouri, S., & Soltoggio, A. (2024). “Statistical Context Detection for Deep Lifelong Reinforcement Learning.” Proceedings of Machine Learning Research, 274, 1013-1031. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219511357&partnerID=40&md5=44236f24c54c2e13e04ef41cc8a97b90�� Context detection involves identifying different tasks within a continuous stream of data. Read More

Machine learning models are increasingly being used to analyze point cloud data, which consists of unordered sets of points, such as 3D scans of objects. To effectively process this type of data, neural networks must be designed to ensure that their predictions remain the same regardless of the order… Read More

Rasdall, Marselle A.; Cho, Chloe; Stahl, Amy N.; Tovar, David A.; Lavin, Patrick; Kerley, Cailey I.; Chen, Qingxia; Ji, Xiangyu; Colyer, Marcus H.; Groves, Lucas; Longmuir, Reid; Chomsky, Amy; Gallagher, Martin J.; Anderson, Adam; Landman, Bennett A.; Rex, Tonia S. “Primary Visual Pathway Changes in Individuals With… Read More

Coursey, Austin; Quinones-Grueiro, Marcos; Biswas, Gautam. “Quantifying the Sim-To-Real Gap in UAV Disturbance Rejection.”��OpenAccess Series in Informatics, vol. 125, 2024, 16,��https://doi.org/10.4230/OASIcs.DX.2024.16.�� �� In many cases, it���s safer and more efficient to develop control systems for drones in simulations… Read More

Maier-Hein, L., Reinke, A., Godau, P., et al. (2024). Metrics reloaded: recommendations for image analysis validation.��Nature Methods, 21(2), 195-212. doi: 10.1038/s41592-023-02151-z��   There is growing evidence that problems with validating machine learning (ML) algorithms are a global issue that���s often overlooked. Read More

Kolouri, S., Naderializadeh, N., Rohde, G. K., & Hoffmann, H. (2021). WASSERSTEIN EMBEDDING FOR GRAPH LEARNING.��ICLR 2021 – 9th International Conference on Learning Representations, 34.��https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150286096&partnerID=40&md5=8b88e110167be2c5fd01da171324d3d6�� We introduce a new method called Wasserstein Embedding for Graph Learning (WEGL), which is a… Read More

Lin, J.-R., Wang, S., Coy, S., Chen, Y.-A., Yapp, C., Tyler, M., Nariya, M. K., Heiser, C. N., Lau, K. S., Santagata, S., & Sorger, P. K. (2023). Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer.��Cell, 186(2), 363-381.e13. doi: 10.1016/j.cell.2022.12.028����… Read More