Machine Learning

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

Davalos, Eduardo; Zhang, Yike; Ashwin, T.S.; Fonteles, Joyce Horn; Timalsina, Umesh; Biswas, Gautam. “3D Gaze Tracking for Studying Collaborative Interactions in Mixed-Reality Environments”. ACM International Conference Proceeding Series, 2024, pp. 175-183. DOI: 10.1145/3686215.3688380.    This study presents a new method for tracking where people are looking in… Read More

Fang, Zihan; Huang, Yu. “‘Math is a pain!’: Understanding challenges and needs of the Machine Learning community on Stack Overflow“. Proceedings of the ACM on Human-Computer Interaction, vol. 8, no. CSCW2, 2024, p. 451. DOI: 10.1145/3686990.    Stack Overflow (SO)… Read More