Deep Learning with Differential Equations

Dear Colleagues,

As artificial intelligence makes progress, deep neural nets are being applied to increasingly complex problem setups. In response to the emerging difficulties of these new setups, deep learning research explores new modeling tools to enhance the predictive power of neural nets. Differential equations are among the new tools that are being incorporated into deep neural net models in various ways. While some approaches use differential equations to build continuous depth into feed-forward neural networks, some others use them to induce a desired regularity or a conservation law into the dynamical system under investigation.

This Special Issue publishes original algorithmic, methodological, and theoretical contributions to artificial intelligence research regarding the incorporation of differential equations into the design of deterministic deep neural networks or the inference of probabilistic deep neural networks. The simplicity of a contribution is a value for us, providing that its significance is demonstrated in rigorous experiments. Theoretical analyses of new aspects of existing approaches and justifications of new approaches using existing theoretical tools are very welcome.

This Special Issue targets a readership from multiple disciplines, including but not limited to machine learning, computer vision, robotics, bioinformatics, as well as business analysis and computational finance.

We look forward to your submissions!

Best regards,

Prof. Dr. Melih Kandemir
Guest Editor

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