Typically, a neural network parameterized by weights $\mathbf{W}$ is a function from an input $x$ to an output $y$. The network has an associated compatibility function $\Psi(y; x, \mathbf{W}) \rightarrow \mathbb{R}^+$ that measures how likely an output y is given an input x under weights $W$.

Source: Gradient-based Inference for Networks with Output Constraints, AAAI 2019 (https://arxiv.org/abs/1707.08608)

What is a compatibility function? What is its utility?

Neural networks are generally deterministic. We are guaranteed to get a specific output for a given input. How do we interpret compatibility function in this context?

  • $\begingroup$ What have you understood about it? Have you read the paper that you're citing? $\endgroup$
    – nbro
    Jan 31, 2022 at 12:20


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