This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …

Experimental results based on offline processing reported at optical conferences
increasingly rely on neural network-based equalizers for accurate data recovery. However …

This paper introduces a novel methodology for developing low-complexity neural network
(NN) based equalizers to address impairments in high-speed coherent optical transmission …

In this work, a perturbation-based neural network (P-NN) scheme with an embedded
bidirectional long short-term memory (biLSTM) layer is investigated to compensate for the …

We experimentally demonstrate the application of a double deep Q-learning network
algorithm (DDQN) for design of a self-starting fiber mode-locked laser. In contrast to the …

In this work, we demonstrate the offline FPGA realization of both recurrent and feedforward
neural network (NN)-based equalizers for nonlinearity compensation in coherent optical …

For the first time, multi-task learning is proposed to improve the flexibility of NN-based
equalizers in coherent systems. A" single" NN-based equalizer improves Q-factor by up to 4 …

Transfer learning (TL) has been demonstrated its feasibility on fast remodeling for fiber
nonlinearity equalization. It will be very efficient with fine-tuning rather than retraining when …

Artificial neural networks (ANNs) have been widely used for nonlinear equalization in short-
reach optical communications due to the superior performance compared with traditional …

Model generalization characterizes the sustainability of machine learning (ML) designs
applied to novel system states and therefore plays a vital role toward the realization of …