Random Hinge Forest for Differentiable Learning

12 Feb 2018Nathan LayAdam P. HarrisonSharon SchreiberGitesh DawerAdrian Barbu

We propose random hinge forests, a simple, efficient, and novel variant of decision forests. Importantly, random hinge forests can be readily incorporated as a general component within arbitrary computation graphs that are optimized end-to-end with stochastic gradient descent or variants thereof... (read more)

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