Learning convex optimization models

Indeed, a convex optimization model can be viewed as a form of energy-based learning where the energy function is convex in the output. For example, input-convex neural networks (ICNNs) [14] can be viewed as a convex optimization model where the energy function is an ICNN.

What is the difference between ReLU and convex optimization?

The results are displayed in Fig 4

The ReLU network achieves a validation loss of 0

071

The trained convex optimization model achieves a validation loss of 0

066, which is close to the validation loss of the underlying model

Additionally, the convex optimization model nearly recovers the true weights

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