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Found 3 papers, 1 papers with code
Leveraging Continuously Differentiable Activation Functions for Learning in Quantized Noisy Environments
Real-world analog systems intrinsically suffer from noise that can impede model convergence and accuracy on a variety of deep learning models.
AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks
AnalogVNN, a simulation framework built on PyTorch which can simulate the effects of optoelectronic noise, limited precision, and signal normalization present in photonic neural network accelerators.
Monadic Pavlovian associative learning in a backpropagation-free photonic network
Here we experimentally demonstrate a form of backpropagation-free learning using a single (or monadic) associative hardware element.
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