A machine learning approach for predicting hidden links in supply chain with graph neural networks

Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

Supply Chain Impact Modelling – Simulation and Machine Learning approach

10.1109/icscan53069.2021.9526484 ◽

2021 ◽

Author(s):

Mohamed Shayeez ◽

Vinay V Panicker

Keyword(s):

Machine Learning ◽

Supply Chain ◽

Learning Approach ◽

Impact Modelling ◽

Modelling Simulation

Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach

International Journal of Production Economics ◽

10.1016/j.ijpe.2019.01.032 ◽

2019 ◽

Vol 211 ◽

pp. 22-33 ◽

Cited By ~ 18

Author(s):

You Zhu ◽

Li Zhou ◽

Chi Xie ◽

Gang-Jin Wang ◽

Truong V. Nguyen

Keyword(s):

Machine Learning ◽

Supply Chain ◽

Credit Risk ◽

Learning Approach ◽

Supply Chain Finance ◽

Ensemble Machine Learning ◽

An Integrated Machine-Learning Approach to Shale-Gas Supply-Chain Optimization and Refrac Candidate Identification

SPE Reservoir Evaluation & Engineering ◽

10.2118/187361-pa ◽

2019 ◽

Vol 22 (04) ◽

pp. 1201-1224 ◽

Cited By ~ 3

Author(s):

Hope I. Asala ◽

Jorge A. Chebeir ◽

Vidhyadhar Manee ◽

Ipsita Gupta ◽

Arash Dahi-Taleghani ◽

...

Keyword(s):

Machine Learning ◽

Supply Chain ◽

Shale Gas ◽

Learning Approach ◽

Supply Chain Optimization ◽

Candidate Identification ◽

Gas Supply

A Machine Learning Approach for Abstraction based on the Idea of Deep Belief Artificial Neural Networks

Procedia Engineering ◽

10.1016/j.proeng.2014.03.147 ◽

2014 ◽

Vol 69 ◽

pp. 1499-1508 ◽

Cited By ~ 1

Author(s):

Florian Neukart ◽

Sorin-Aurel Moraru

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Learning Approach ◽

Artificial Neural

Machine learning approach to OAM beam demultiplexing via convolutional neural networks

Applied Optics ◽

10.1364/ao.56.003386 ◽

2017 ◽

Vol 56 (12) ◽

pp. 3386 ◽

Cited By ~ 50

Author(s):

Timothy Doster ◽

Abbie T. Watnik

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Learning Approach ◽

Artificial neural networks for the prediction of solvation energies based on experimental and computational data

Physical Chemistry Chemical Physics ◽

10.1039/d0cp03701j ◽

2020 ◽

Vol 22 (42) ◽

pp. 24359-24364

Author(s):

Jiyoung Yang ◽

Matthias J. Knape ◽

Oliver Burkert ◽

Virginia Mazzini ◽

Alexander Jung ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Ion Pair ◽

Learning Approach ◽

Solvation Energies ◽

Computational Data ◽

Artificial Neural

We present a machine learning approach based on artificial neural networks for the prediction of ion pair solvation energies.

Assessment of bilateral knee pain from MR imaging using deep neural networks

10.1101/463497 ◽

2018 ◽

Author(s):

Gary H. Chang ◽

David T. Felson ◽

Shangran Qiu ◽

Terence D. Capellini ◽

Vijaya B. Kolachalama

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Neural Networks ◽

Deep Learning ◽

Knee Pain ◽

Deep Neural Networks ◽

Learning Approach ◽

Mr Images ◽

Bilateral Knee ◽

ABSTRACTBackground and objectiveIt remains difficult to characterize pain in knee joints with osteoarthritis solely by radiographic findings. We sought to understand how advanced machine learning methods such as deep neural networks can be used to analyze raw MRI scans and predict bilateral knee pain, independent of other risk factors.MethodsWe developed a deep learning framework to associate information from MRI slices taken from the left and right knees of subjects from the Osteoarthritis Initiative with bilateral knee pain. Model training was performed by first extracting features from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices. The extracted features from all the 2D slices were subsequently combined to directly associate using a fused deep neural network with the output of interest as a binary classification problem.ResultsThe deep learning model resulted in predicting bilateral knee pain on test data with 70.1% mean accuracy, 51.3% mean sensitivity, and 81.6% mean specificity. Systematic analysis of the predictions on the test data revealed that the model performance was consistent across subjects of different Kellgren-Lawrence grades.ConclusionThe study demonstrates a proof of principle that a machine learning approach can be applied to associate MR images with bilateral knee pain.SIGNIFICANCE AND INNOVATIONKnee pain is typically considered as an early indicator of osteoarthritis (OA) risk. Emerging evidence suggests that MRI changes are linked to pre-clinical OA, thus underscoring the need for building image-based models to predict knee pain. We leveraged a state-of-the-art machine learning approach to associate raw MR images with bilateral knee pain, independent of other risk factors.

Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields

Physical Chemistry Chemical Physics ◽

10.1039/d0cp03694c ◽

2020 ◽

Vol 22 (40) ◽

pp. 22889-22899

Author(s):

Xian Wang ◽

Anshuman Kumar ◽

Christian R. Shelton ◽

Bryan M. Wong

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Inverse Problem ◽

Quantum Dynamics ◽

Deep Neural Networks ◽

Cost Effective ◽

Quantum Systems ◽

Time Dependent ◽

Learning Approach ◽

Deep neural networks are a cost-effective machine-learning approach for solving the inverse problem of constructing electromagnetic fields that enable desired transitions in quantum systems.

On the Potential for Open-Endedness in Neural Networks

Artificial Life ◽

10.1162/artl_a_00286 ◽

2019 ◽

Vol 25 (2) ◽

pp. 145-167 ◽

Cited By ~ 2

Author(s):

Nicholas Guttenberg ◽

Nathaniel Virgo ◽

Alexandra Penn

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Gradient Descent ◽

The Other ◽

Learning Approach ◽

Natural Evolution ◽

Machine Learning Methods ◽

Common Barriers

Natural evolution gives the impression of leading to an open-ended process of increasing diversity and complexity. If our goal is to produce such open-endedness artificially, this suggests an approach driven by evolutionary metaphor. On the other hand, techniques from machine learning and artificial intelligence are often considered too narrow to provide the sort of exploratory dynamics associated with evolution. In this article, we hope to bridge that gap by reviewing common barriers to open-endedness in the evolution-inspired approach and how they are dealt with in the evolutionary case—collapse of diversity, saturation of complexity, and failure to form new kinds of individuality. We then show how these problems map onto similar ones in the machine learning approach, and discuss how the same insights and solutions that alleviated those barriers in evolutionary approaches can be ported over. At the same time, the form these issues take in the machine learning formulation suggests new ways to analyze and resolve barriers to open-endedness. Ultimately, we hope to inspire researchers to be able to interchangeably use evolutionary and gradient-descent-based machine learning methods to approach the design and creation of open-ended systems.