| Author/Title |
Research Type |
Related Fields |
| Le, V., Nagpal, C., & Dubrawski, A. (2023). Identification of patients with stable coronary artery disease who benefit from ACE inhibitors using Cox mixture model for heterogeneous treatment effects. Journal of Critical Care, 74, 154208. |
Publications, Articles |
Machine Learning |
| Liu, Y.H., Smith, S., Mihalas S., Shea-Brown E., Sumbul U., “Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators”, Advances in Neural Information Processing Systems, 2022. |
Publications, Articles |
Applied Mathematics, Computer Science, Machine Learning, Mathematical Neuroscience |
| Liu, Y.H., Ghosh A., Richards B. A., Shea-Brown E., Lajoie G., “Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules”, Advances in Neural Information Processing Systems, 2022. |
Publications, Articles |
Applied Linear Algebra, Applied Mathematics, Computer Science, Dynamical Systems, Machine Learning, Mathematical Neuroscience |
| Maass, K., Aravkin, A., & Kim, M. (2021). A feasibility study of a hyperparameter tuning approach to automated inverse planning in radiotherapy. arXiv preprint arXiv: arXiv:2105.07024. |
Publications, Articles |
Applied Mathematics, Cancer Modeling, Machine Learning, Optimization and Variational Analysis |
| Liu, Y.H., Smith, S.J., Mihalas S., Shea-Brown E., Sumbul U., “Cell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network”, Proceedings of the National Academy of Sciences, 2021. |
Publications, Articles |
Computer Science, Machine Learning, Mathematical Neuroscience |
| Liu, Y.H., Smith, S.J., Mihalas S., Shea-Brown E., Sumbul U., “A solution to temporal credit assignment using cell-type-specific modulatory signals”, bioRxiv, 2020. |
Publications, Articles |
Machine Learning, Mathematical Neuroscience |
Ahlstrom, Austin, "Computational Regiospecific Analysis of Brain Lipidomic Profiles" (2019). Undergraduate Honors Theses. 70.
https://scholarsarchive.byu.edu/studentpub_uht/70 |
Publications, Articles |
Applied Mathematics, Biochemistry, Machine Learning, Mathematical Biology |
| Liu, Y.H., Baratin A., Cornford J., Mihalas S., Shea-Brown E., Lajoie G., “How connectivity structure shapes rich and lazy learning in neural circuits”, International Conference on Learning Representations (ICLR), 2024 (accepted). |
Publications, Articles |
Applied Mathematics, Computer Science, Machine Learning, Mathematical Neuroscience |
