2 papers
cs.LG2026
Low-power analogue neural networks with trainable nonlinear connections for continuous control
Ian T. Vidamour, Fernando Aguirre, Thomas J. Hayward +13
Physical neural networks promise low-power machine learning by computing directly with analogue device physics, but most architectures force nonlinear device responses to act as sc…
cs.ET2024
Nonideality-aware training makes memristive networks more robust to adversarial attacks
Dovydas Joksas, Luis Muñoz-González, Emil Lupu +1
Neural networks are now deployed in a wide number of areas from object classification to natural language systems. Implementations using analog devices like memristors promise bett…