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Found 4 papers, 3 papers with code
Genetically programmable optical random neural networks
Today, machine learning tools, particularly artificial neural networks, have become crucial for diverse applications.
Machine Learning with Chaotic Strange Attractors
Here, we present an analog computing method that harnesses chaotic nonlinear attractors to perform machine learning tasks with low power consumption.
Scalable Optical Learning Operator
Today's heavy machine learning tasks are fueled by large datasets.
Competing Neural Networks for Robust Control of Nonlinear Systems
The output of physical systems is often accessible by measurements such as the 3D position of a robotic arm actuated by many actuators or the speckle patterns formed by shining the spot of a laser pointer on a wall.
