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In recent years, the use of machine learning for solving complex problems has spread like wildfire. Specifically, machine learning has proved to be very effective in generating embeddings, both for tasks related to simple words/images and for those involving complex data arising in the domains of biology and chemistry. Inspired by these breakthroughs, we look at the problem of generating embeddings from an underlying dataset of circuits and prove their utility on several posterior tasks.

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