( Personal message: I will at ICLR next week, let's grab some coffee if you are there. )
F2F: A Library For Fast Kernel Expansions by Joachim Curto, Irene Zarza, Feng Yang, Alexander J. Smola, Luc Van Gool
F2F is a C++ library for large-scale machine learning. It contains a CPU optimized implementation of the Fastfood algorithm, that allows the computation of approximated kernel expansions in loglinear time. The algorithm requires to compute the product of Walsh-Hadamard Transform (WHT) matrices. A cache friendly SIMD Fast Walsh-Hadamard Transform (FWHT) that achieves compelling speed and outperforms current state-of-the-art methods has been developed. F2F allows to obtain non-linear classification combining Fastfood and a linear classifier.
I am told by one of the author that the library should be out at some point in time.
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