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In mathematics, the Hessian matrix, Hessian or (less commonly) Hesse matrix is a square matrix of second-order partial derivatives of a scalar-valued function, or scalar field. It describes the local curvature of a function of many variables. The Hessian matrix was developed in the 19th century by the German mathematician Ludwig Otto Hesse and later…
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| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Hessian matrix | is a | symmetric matrix by the symmetry of second derivatives.The determinant of the Hessian matrix is called the Hessian determinant.The Hessian matrix of a function f | 0.90 | text |
| Hessian matrix | is a | covariant | 0.90 | text |
| the loss functions of neural nets | instance of | which is infeasible for high-dimensional functions | 0.80 | text |
| conditional random fields | instance of | which is infeasible for high-dimensional functions | 0.80 | text |
| and other statistical models with large numbers of parameters | instance of | which is infeasible for high-dimensional functions | 0.80 | text |
| Hessian matrix | has application | The Hessian | 0.60 | section |
| Hessian matrix | has application | Laplacian | 0.60 | section |
| Hessian matrix | has application | Gaussian | 0.60 | section |
| Hessian matrix | has application | LoG | 0.60 | section |
| Hessian matrix | has application | Hessian | 0.60 | section |
| Hessian matrix | has application | DoH | 0.60 | section |
| Hessian matrix | has application | It | 0.60 | section |
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