This repository contains papers and codes (if available) for the deep equilibrium models. In DEQ models, the output of the model must be a fixed point of some learnable transformation follow a fixed-point theorem.
| Paper Title | Where | Architecture | Code |
|---|---|---|---|
| Tutorial Deep Implicit Layers - Neural ODEs, Deep Equilibrium Models, and Beyond | NeurIPS | Survey | - |
| Deep Equilibrium Models | NeurIPS 2019 | - | - |
| Multiscale Deep Equilibrium Models | NeurIPS 2020 | - | - |
| Monotone Operator Equilibrium Networks | NeurIPS 2020 | - | - |
| Lipschitz Bounded Equilibrium Networks | Arxiv | - | - |
| Implicit Deep Learning | SIAM Journal on Mathematics of Data Science | - | - |
| ----------------------------------------- | ------------ | --------- | ----------- |
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