Deep learning methods for inverse problems

Kamyab, Shima; Azimifar, Zohreh; Sabzi, Rasool; Fieguth, Paul

doi:10.7717/peerj-cs.951

Cited by 13 publications

(6 citation statements)

References 47 publications

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“…In recent years, deep learning has made significant advancements, particularly in fields like image recognition and natural language processing ( Fujiyoshi, Hirakawa & Yamashita, 2019 ; Kamyab et al, 2022 ). For instance, convolutional neural networks (CNNs) have made breakthroughs in the application of image recognition ( Rawat & Wang, 2017 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Unlocking the potential of LSTM for accurate salary prediction with MLE, Jeffreys prior, and advanced risk functions

Li,

Majid,

Ding

2024

PeerJ Computer Science

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This article aims to address the challenge of predicting the salaries of college graduates, a subject of significant practical value in the fields of human resources and career planning. Traditional prediction models often overlook diverse influencing factors and complex data distributions, limiting the accuracy and reliability of their predictions. Against this backdrop, we propose a novel prediction model that integrates maximum likelihood estimation (MLE), Jeffreys priors, Kullback-Leibler risk function, and Gaussian mixture models to optimize LSTM models in deep learning. Compared to existing research, our approach has multiple innovations: First, we successfully improve the model’s predictive accuracy through the use of MLE. Second, we reduce the model’s complexity and enhance its interpretability by applying Jeffreys priors. Lastly, we employ the Kullback-Leibler risk function for model selection and optimization, while the Gaussian mixture models further refine the capture of complex characteristics of salary distribution. To validate the effectiveness and robustness of our model, we conducted experiments on two different datasets. The results show significant improvements in prediction accuracy, model complexity, and risk performance. This study not only provides an efficient and reliable tool for predicting the salaries of college graduates but also offers robust theoretical and empirical foundations for future research in this field.

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Section: Literature Reviewmentioning

confidence: 99%

Unlocking the potential of LSTM for accurate salary prediction with MLE, Jeffreys prior, and advanced risk functions

Li,

Majid,

Ding

2024

PeerJ Computer Science

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“…In Fig. 3, we show the distribution of errors on the mesh D tr (16) for the training dataset. Here every point is colored according to the largest of all errors (15) for the 3 concentrations.…”

Section: Data Generationmentioning

confidence: 99%

“…An example of an inverse problem -the determination of parameters for the reaction-diffusion equation -is given in [22]. A review of neural networks for solving linear inverse problems is given in [16].…”

Section: Introductionmentioning

confidence: 99%

Applying artificial neural networks to solve the inverse problem of evaluating concentrations in multianalyte mixtures from biosensor signals

Dapšys,

Čiegis,

Starikovičius

2023

NAMC

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We investigate the ill-posedness of the inverse biosensor problem when the biosensor signals are corrupted by noise. To solve the problem, we employ feed-forward and convolutional neural networks. Computational experiments were performed with different levels of additive and multiplicative noises for the batch and flow injection analysis modes of the biosensor. Obtained results show that the largest errors of recovered concentrations are located on the edges of the training domain. We have found that the inverse problem is less ill-posed in the flow injection analysis mode and concentrations can be reliably recovered for higher levels of noise compared to the batch mode. This finding is confirmed by the application of the DIRECT global optimization method to the considered inverse biosensor problem.

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“…Hybrid inverse modeling algorithms combining traditional approaches with machine learning techniques are promising to improve inverse modeling results. A review of the applications of machine learning methods to inverse problems can be found in [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

Penenko,

Emelyanov,

Rusin

et al. 2023

Mathematics

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Hybrid approaches combining machine learning with traditional inverse problem solution methods represent a promising direction for the further development of inverse modeling algorithms. The paper proposes an approach to emission source identification from measurement data for advection–diffusion–reaction models. The approach combines general-type source identification and post-processing refinement: first, emission source identification by measurement data is carried out by a sensitivity operator-based algorithm, and then refinement is done by incorporating a priori information about unknown sources. A general-type distributed emission source identified at the first stage is transformed into a localized source consisting of multiple point-wise sources. The second, refinement stage consists of two steps: point-wise source localization and emission rate estimation. Emission source localization is carried out using deep learning with convolutional neural networks. Training samples are generated using a sensitivity operator obtained at the source identification stage. The algorithm was tested in regional remote sensing emission source identification scenarios for the Lake Baikal region and was able to refine the emission source reconstruction results. Hence, the aggregates used in traditional inverse problem solution algorithms can be successfully applied within machine learning frameworks to produce hybrid algorithms.

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Deep learning methods for inverse problems

Cited by 13 publications

References 47 publications

Unlocking the potential of LSTM for accurate salary prediction with MLE, Jeffreys prior, and advanced risk functions

Unlocking the potential of LSTM for accurate salary prediction with MLE, Jeffreys prior, and advanced risk functions

Applying artificial neural networks to solve the inverse problem of evaluating concentrations in multianalyte mixtures from biosensor signals

Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

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