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Found 14 papers, 5 papers with code
Signal processing after quadratic random sketching with optical units
In this context, the possibility of performing data processing (such as pattern detection or classification) directly in the sketched domain without accessing the original data was previously achieved for linear random sketching methods and compressive sensing.
Signal processing with optical quadratic random sketches
In this context, the possibility of performing data processing (such as pattern detection or classification) directly in the sketched domain without accessing the original data was previously achieved for linear random sketching methods and compressive sensing.
Photonic co-processors in HPC: using LightOn OPUs for Randomized Numerical Linear Algebra
Randomized Numerical Linear Algebra (RandNLA) is a powerful class of methods, widely used in High Performance Computing (HPC).
Hardware Beyond Backpropagation: a Photonic Co-Processor for Direct Feedback Alignment
We present a photonic accelerator for Direct Feedback Alignment, able to compute random projections with trillions of parameters.
Online Change Point Detection in Molecular Dynamics With Optical Random Features
In this work, we focus on the identification of such events given many noisy observables.
Light-in-the-loop: using a photonics co-processor for scalable training of neural networks
As neural networks grow larger and more complex and data-hungry, training costs are skyrocketing.
Fast Optical System Identification by Numerical Interferometry
Our method simplifies the calibration of optical transmission matrices from a quadratic to a linear inverse problem by first recovering the phase of the measurements.
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.
Don't take it lightly: Phasing optical random projections with unknown operators
A signal of interest $\mathbf{\xi} \in \mathbb{R}^N$ is mixed by a random scattering medium to compute the projection $\mathbf{y} = \mathbf{A} \mathbf{\xi}$, with $\mathbf{A} \in \mathbb{C}^{M \times N}$ being a realization of a standard complex Gaussian iid random matrix.
Machine learning and the physical sciences
Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years.
Computational Physics Cosmology and Nongalactic Astrophysics Disordered Systems and Neural Networks High Energy Physics - Theory Quantum Physics
Random Projections through multiple optical scattering: Approximating kernels at the speed of light
Random projections have proven extremely useful in many signal processing and machine learning applications.
Intensity-only optical compressive imaging using a multiply scattering material and a double phase retrieval approach
In this paper, the problem of compressive imaging is addressed using natural randomization by means of a multiply scattering medium.
Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit
We investigate the methods that simultaneously enforce sparsity and low-rank structure in a matrix as often employed for sparse phase retrieval problems or phase calibration problems in compressive sensing.
Dictionary Subselection Using an Overcomplete Joint Sparsity Model
As this model is often unknown for many classes of the signals, we need to select such a model based on the domain knowledge or using some exemplar signals.
