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Found 9 papers, 4 papers with code
Extension of Recurrent Kernels to different Reservoir Computing topologies
Reservoir Computing (RC) has become popular in recent years due to its fast and efficient computational capabilities.
Mechanical Artifacts in Optical Projection Tomography: Classification and Automatic Calibration
Then, we introduce a calibration algorithm that recovers the unknown system parameters fed into the final 3D iterative reconstruction algorithm for a distortion-free volumetric image.
Asymptotic Stability in Reservoir Computing
Reservoir Computing is a class of Recurrent Neural Networks with internal weights fixed at random.
Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors
In this work, we present a Bayesian reconstruction framework for nonlinear imaging models where we specify the prior knowledge on the image through a deep generative model.
Reservoir Computing meets Recurrent Kernels and Structured Transforms
Reservoir Computing is a class of simple yet efficient Recurrent Neural Networks where internal weights are fixed at random and only a linear output layer is trained.
Kernel computations from large-scale random features obtained by Optical Processing Units
Approximating kernel functions with random features (RFs)has been a successful application of random projections for nonparametric estimation.
Spectral Method for Multiplexed Phase Retrieval and Application in Optical Imaging in Complex Media
We introduce a generalized version of phase retrieval called multiplexed phase retrieval.
Scaling up Echo-State Networks with multiple light scattering
As a proof of concept, binary networks have been successfully trained to predict the chaotic Mackey-Glass time series.
Experimental robustness of Fourier Ptychography phase retrieval algorithms
Both noise (e. g. Poisson noise) and model mis-match errors are shown to scale with intensity.
