10 citations · 26 across the 8 of their papers we have counts for
8 papers
SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networks
Sreyes Venkatesh, Razvan Marinescu, Jason K. Eshraghian
Weight quantization is used to deploy high-performance deep learning models on resource-limited hardware, enabling the use of low-precision integers for storage and computation. Sp…
Addressing cognitive bias in medical language models
Samuel Schmidgall, Carl Harris, Ime Essien +7
There is increasing interest in the application large language models (LLMs) to the medical field, in part because of their impressive performance on medical exam questions. While…
Multitask Deep Learning for Accurate Risk Stratification and Prediction of Next Steps for Coronary CT Angiography Patients
Juan Lu, Mohammed Bennamoun, Jonathon Stewart +5
Diagnostic investigation has an important role in risk stratification and clinical decision making of patients with suspected and documented Coronary Artery Disease (CAD). However,…
Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals
Luis Fernando Herbozo Contreras, Nhan Duy Truong, Jason K. Eshraghian +4
Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnorma…
PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators
Ziyu Wang, Yuting Wu, Yongmo Park +4
Analog compute-in-memory (CIM) systems are promising for deep neural network (DNN) inference acceleration due to their energy efficiency and high throughput. However, as the use of…
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,…