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Found 2 papers, 1 papers with code
A Preconditioned Interior Point Method for Support Vector Machines Using an ANOVA-Decomposition and NFFT-Based Matrix-Vector Products
In this paper we consider the numerical solution to the soft-margin support vector machine optimization problem.
Constructing Gradient Controllable Recurrent Neural Networks Using Hamiltonian Dynamics
The key benefit of this approach is that the corresponding RNN inherits the favorable long time properties of the Hamiltonian system, which in turn allows us to control the hidden states gradient with a hyperparameter of the Hamiltonian RNN architecture.
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