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Found 8 papers, 2 papers with code
Optical Diffusion Models for Image Generation
Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution.
Training of Physical Neural Networks
Research over the past few years has shown that the answer to all these questions is likely "yes, with enough research": PNNs could one day radically change what is possible and practical for AI systems.
Nonlinear Processing with Linear Optics
In this study, we present a novel framework that uses multiple scattering that is capable of synthesizing programmable linear and nonlinear transformations concurrently at low optical power by leveraging the nonlinear relationship between the scattering potential, represented by data, and the scattered field.
Forward-Forward Training of an Optical Neural Network
Neural networks (NN) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations.
Nonlinear Optical Data Transformer for Machine Learning
A dataset is encoded digitally on the spectrum of a femtosecond pulse which is then launched in the waveguide.
Variational framework for partially-measured physical system control: examples of vision neuroscience and optical random media
To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured.
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.
