Principled Machine Learning

Raykov, Yordan P.; Saad, David

doi:10.1109/jstqe.2022.3186798

IEEE J. Select. Topics Quantum Electron.

2022

DOI: 10.1109/jstqe.2022.3186798

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Principled Machine Learning

Yordan P. Raykov

David Saad

Abstract: We introduce the underlying concepts which give rise to some of the commonly used machine learning methods ideas, excluding deep-learning machines and neural networks. We point to their advantages, limitations and potential use in various areas of photonics. The main methods covered include parametric and non-parametric regression and classification techniques, kernel-based methods and support vector machines, decision trees, probabilistic models, Bayesian graphs, mixture models, Gaussian processes, message pa… Show more

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Cited by 2 publications

References 103 publications

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Artificial neural networks for photonic applications—from algorithms to implementation: tutorial

Souza¹,

Manuylovich²,

Prilepsky³

et al. 2023

Adv. Opt. Photon.

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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 and applied mathematics. We focus here on the research areas at the interface between these disciplines, attempting to find the right balance between technical details specific to each domain and overall clarity. First, we briefly recall key properties and peculiarities of some core neural network types, which we believe are the most relevant to photonics, also linking the layer’s theoretical design to some photonics hardware realizations. After that, we elucidate the question of how to fine-tune the selected model’s design to perform the required task with optimized accuracy. Then, in the review part, we discuss recent developments and progress for several selected applications of neural networks in photonics, including multiple aspects relevant to optical communications, imaging, sensing, and the design of new materials and lasers. In the following section, we put a special emphasis on how to accurately evaluate the complexity of neural networks in the context of the transition from algorithms to hardware implementation. The introduced complexity characteristics are used to analyze the applications of neural networks in optical communications, as a specific, albeit highly important example, comparing those with some benchmark signal-processing methods. We combine the description of the well-known model compression strategies used in machine learning, with some novel techniques introduced recently in optical applications of neural networks. It is important to stress that although our focus in this tutorial–review is on photonics, we believe that the methods and techniques presented here can be handy in a much wider range of scientific and engineering applications.

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Artificial neural networks for photonic applications—from algorithms to implementation: tutorial

Souza¹,

Manuylovich²,

Prilepsky³

et al. 2023

Adv. Opt. Photon.

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show abstract

Knowledge distillation aided by probability information enabled efficient joint monitoring of SNRL and SNRNL for WDM systems

Wang,

Bai,

Bai

et al. 2024

Opt. Express

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For wavelength division multiplexing (WDM) systems, excessive linear and nonlinear noise will seriously decrease the quality of optical signals, and the effective joint monitoring scheme can prevent the degradation of system performance due to noise accumulation. In this paper, we propose a probability information assisted knowledge distillation (PIAKD) scheme that achieves intelligent joint monitoring for linear signal-to-noise ratio (SNRL) and nonlinear signal-to-noise ratio (SNRNL) in WDM systems. Under the condition of multi-task regression, outputs are independent and continuous, PIAKD addresses the longstanding challenge that the student model fails to effectively learn knowledge from the teacher model by introducing probability information into the loss function. The effectiveness of the scheme is verified by WDM simulation and experiment system which has a symbol rate of 28 GBaud per channel. The simulation results demonstrate that the overall mean absolute error (MAE) for jointing SNRL and SNRNL monitoring of the student model after PIAKD is reduced by 0.08 dB and 0.09 dB, corresponding to 32% and 34% error reductions respectively. Furthermore, when compared with the KD scheme without probability information, our scheme also reduces the overall MAE of SNRL and SNRNL by 16% and 11%, respectively. The results of the experiment reveal that the estimated MAE could be reduced by 0.13 dB and 0.16 dB, respectively, corresponding to error reductions of 17% and 18%. Moreover, the floating-point operations (FLOPs) and parameters (Params) of the student model is only 3.30 M and 0.0015 M, respectively, which is both significantly lower than the complexity of existing joint monitoring schemes for SNRL and SNRNL.

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Principled Machine Learning

Cited by 2 publications

References 103 publications

Artificial neural networks for photonic applications—from algorithms to implementation: tutorial

Artificial neural networks for photonic applications—from algorithms to implementation: tutorial

Knowledge distillation aided by probability information enabled efficient joint monitoring of SNRL and SNRNL for WDM systems

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