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Found 5 papers, 1 papers with code
Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning
We present and experimentally evaluate using transfer learning to address experimental data scarcity when training neural network (NN) models for Mach-Zehnder interferometer mesh-based optical matrix multipliers.
Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning
We demonstrate transfer learning-assisted neural network models for optical matrix multipliers with scarce measurement data.
Data-driven Modeling of Mach-Zehnder Interferometer-based Optical Matrix Multipliers
Photonic integrated circuits are facilitating the development of optical neural networks, which have the potential to be both faster and more energy efficient than their electronic counterparts since optical signals are especially well-suited for implementing matrix multiplications.
Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs
We experimentally compare simple physics-based vs. data-driven neural-network-based models for offline training of programmable photonic chips using Mach-Zehnder interferometer meshes.
Captum: A unified and generic model interpretability library for PyTorch
The library contains generic implementations of a number of gradient and perturbation-based attribution algorithms, also known as feature, neuron and layer importance algorithms, as well as a set of evaluation metrics for these algorithms.
