| Brain-inspired learning in artificial neural networks: a review S Schmidgall, J Achterberg, T Miconi, L Kirsch, R Ziaei, S Hajiseyedrazi, ... arXiv preprint arXiv:2305.11252, 2023 | 14 | 2023 |
| Neurobench: Advancing neuromorphic computing through collaborative, fair and representative benchmarking J Yik, SH Ahmed, Z Ahmed, B Anderson, AG Andreou, C Bartolozzi, ... arXiv preprint arXiv:2304.04640, 2023 | 11 | 2023 |
| SpikePropamine: Differentiable Plasticity in Spiking Neural Networks S Schmidgall, J Ashkanazy, W Lawson, J Hays Frontiers in Neurorobotics, 2021 | 7 | 2021 |
| Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks S Schmidgall Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020 | 7 | 2020 |
| Meta-SpikePropamine: Learning to learn with synaptic plasticity in spiking neural networks S Schmidgall, J Hays Frontiers in Neuroscience 17, 671, 2023 | 6* | 2023 |
| Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks S Schmidgall, J Hays Proceedings of the 2022 Neuro-Inspired Computing Elements Conference, 2021 | 5 | 2021 |
| Language models are susceptible to incorrect patient self-diagnosis in medical applications R Ziaei, S Schmidgall NeurIPS 2023 Deep Generative Models for Healthcare Workshop, 2023, 2023 | 3 | 2023 |
| Biological connectomes as a representation for the architecture of artificial neural networks S Schmidgall, C Schuman, M Parsa Proceedings of the 2023 AAAI Conference on Artificial Intelligence "Systems …, 2023 | 3 | 2023 |
| Addressing cognitive bias in medical language models S Schmidgall, C Harris, I Essien, D Olshvang, T Rahman, JW Kim, R Ziaei, ... arXiv preprint arXiv:2402.08113, 2024 | 2 | 2024 |
| Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots S Schmidgall, A Krieger, J Eshraghian 2024 IEEE International Conference on Robotics and Automation (ICRA), 2023 | 2 | 2023 |
| Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks S Schmidgall, J Hays Proceedings of the 2023 International Conference on Neuromorphic Systems, 2023 | 2 | 2023 |
| General-purpose foundation models for increased autonomy in robot-assisted surgery S Schmidgall, JW Kim, A Kuntz, AE Ghazi, A Krieger arXiv preprint arXiv:2401.00678, 2024 | 1 | 2024 |
| Evolutionary self-replication as a mechanism for producing artificial intelligence S Schmidgall, J Hays arXiv preprint arXiv:2109.08057, 2021 | 1 | 2021 |
| Optimal Localized Trajectory Planning of Multiple Non-holonomic Vehicles A Lukyanenko, H Camphire, A Austin, S Schmidgall, D Soudbakhsh 2021 IEEE Conference on Control Technology and Applications (CCTA), 820-825, 2021 | 1 | 2021 |
| Self-Constructing Neural Networks through Random Mutation S Schmidgall ICLR 2021 Never-Ending Reinforcement Learning Workshop, 2021 | 1 | 2021 |
| Robots learning to imitate surgeons—challenges and possibilities S Schmidgall, JW Kim, A Krieger Nature Reviews Urology, 1-2, 2024 | | 2024 |
| General surgery vision transformer: A video pre-trained foundation model for general surgery S Schmidgall, JW Kim, J Jopling, A Krieger arXiv preprint arXiv:2403.05949, 2024 | | 2024 |
| Learning a Library of Surgical Manipulation Skills for Robotic Surgery JW Kim, S Schmidgall, A Krieger, M Kobilarov Bridging the Gap between Cognitive Science and Robot Learning in the Real …, 2024 | | 2024 |
| Locked fronts in a discrete time discrete space population model M Holzer, Z Richey, W Rush, S Schmidgall Journal of mathematical biology 85 (4), 39, 2022 | | 2022 |
Rojin ZiaeiStudent, University of MarylandVerified email at terpmail.umd.edu
