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CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.

  • Updated Sep 8, 2020
  • C++
gregdhill
gregdhill commented Oct 7, 2018

In the paper, a distinct subset of cryptographic algorithms & opcodes were correlated which resulted in our model achieving 96% accuracy. It would be advantageous to explore different settings which could further improve accuracy / generalization:

  • Trial different feature engineering techniques.
  • Build more datasets with a different number of embeddi

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