Samuel Schmidgall

According to our database, Samuel Schmidgall authored at least 21 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Evaluation and mitigation of cognitive biases in medical language models.

Samuel Schmidgall, Carl Harris, Ime Essien, Daniel Olshvang, Tawsifur Rahman, Ji Woong Kim, Rojin Ziaei, Jason Eshraghian, Peter Abadir, Rama Chellappa

npj Digit. Medicine, 2024

SurGen: Text-Guided Diffusion Model for Surgical Video Generation.

Joseph Cho, Samuel Schmidgall, Cyril Zakka, Mrudang Mathur, Rohan Shad, William Hiesinger

CoRR, 2024

GP-VLS: A general-purpose vision language model for surgery.

Samuel Schmidgall, Joseph Cho, Cyril Zakka, William Hiesinger

CoRR, 2024

Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks.

Ji Woong Kim, Tony Z. Zhao, Samuel Schmidgall, Anton Deguet, Marin Kobilarov, Chelsea Finn, Axel Krieger

CoRR, 2024

AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments.

Samuel Schmidgall, Rojin Ziaei, Carl Harris, Eduardo Pontes Reis, Jeffrey Jopling, Michael Moor

CoRR, 2024

General surgery vision transformer: A video pre-trained foundation model for general surgery.

Samuel Schmidgall, Ji Woong Kim, Jeffrey Jopling, Axel Krieger

CoRR, 2024

Addressing cognitive bias in medical language models.

Samuel Schmidgall, Carl Harris, Ime Essien, Daniel Olshvang, Tawsifur Rahman, Ji Woong Kim, Rojin Ziaei, Jason Eshraghian, Peter Abadir, Rama Chellappa

CoRR, 2024

General-purpose foundation models for increased autonomy in robot-assisted surgery.

Samuel Schmidgall, Ji Woong Kim, Alan Kuntz, Ahmed Ezzat Ghazi, Axel Krieger

CoRR, 2024

Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots.

Samuel Schmidgall, Axel Krieger, Jason Eshraghian

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

2023

Language models are susceptible to incorrect patient self-diagnosis in medical applications.

Rojin Ziaei, Samuel Schmidgall

CoRR, 2023

Brain-inspired learning in artificial neural networks: a review.

Samuel Schmidgall, Jascha Achterberg, Thomas Miconi, Louis Kirsch, Rojin Ziaei, S. Pardis Hajiseyedrazi, Jason Eshraghian

CoRR, 2023

Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks.

Samuel Schmidgall, Joe Hays

Proceedings of the 2023 International Conference on Neuromorphic Systems, 2023

2022

Biological connectomes as a representation for the architecture of artificial neural networks.

Samuel Schmidgall, Catherine D. Schuman, Maryam Parsa

CoRR, 2022

Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks.

Samuel Schmidgall, Joe Hays

CoRR, 2022

Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks.

Samuel Schmidgall, Joe Hays

Proceedings of the NICE 2022: Neuro-Inspired Computational Elements Conference, 2022

2021

SpikePropamine: Differentiable Plasticity in Spiking Neural Networks.

Samuel Schmidgall, Julia Ashkanazy, Wallace Lawson, Joe Hays

Frontiers Neurorobotics, 2021

Self-Replicating Neural Programs.

Samuel Schmidgall

CoRR, 2021

Evolutionary Self-Replication as a Mechanism for Producing Artificial Intelligence.

Samuel Schmidgall, Joseph Hays

CoRR, 2021

Self-Constructing Neural Networks Through Random Mutation.

Samuel Schmidgall

CoRR, 2021

Optimal Localized Trajectory Planning of Multiple Non-holonomic Vehicles.

Anton Lukyanenko, Heath Camphire, Avery Austin, Samuel Schmidgall, Damoon Soudbakhsh

Proceedings of the IEEE Conference on Control Technology and Applications, 2021

2020

Adaptive reinforcement learning through evolving self-modifying neural networks.

Samuel Schmidgall

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020