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The proposed new neural network generates a sum of relative derivative polynomial terms as a general differential equation model description.
When this very general differential geometry came down to two-dimensional surfaces of constant curvature, it revealed excellent models for non-Euclidean geometries.
((ucdotnabla)), div and △ are general differential operators.
In this section, we conduct the error analysis for the general differential private ERM algorithm (2).
Furthermore, the conditions given for the functions and lead to studying more general differential equations.
Here we present a general convergence result for the general differential private ERM learning algorithms.
Consider the general differential equations with state-dependent impulse textstylebegin{cases} left.
However, it is very difficult to obtain a related estimate for general differential equations.
For general differential equations this goal is out of reach at present.
Consider the general differential system with state-dependent impulse textstylebegin{cases} left.
In this paper, we consider the general differential difference cases in some sense and obtain some results as follows.
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