29 citations · 104 across the 23 of their papers we have counts for
Showing 2023Show all
3 papers · 1 filter
cs.CL2023★ 2 cited
Language models are susceptible to incorrect patient self-diagnosis in medical applications
Rojin Ziaei, Samuel Schmidgall
Large language models (LLMs) are becoming increasingly relevant as a potential tool for healthcare, aiding communication between clinicians, researchers, and patients. However, tra…
cs.RO2023★ 1 cited
Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks
Samuel Schmidgall, Joe Hays
Legged robots operating in real-world environments must possess the ability to rapidly adapt to unexpected conditions, such as changing terrains and varying payloads. This paper in…
cs.NE2023★ 5 cited
Brain-inspired learning in artificial neural networks: a review
Samuel Schmidgall, Jascha Achterberg, Thomas Miconi +4
Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation,…