Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Monotone operator equilibrium networks
Authors: Ezra Winston, J. Zico Kolter
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To test the expressive power and training stability of mon DEQs, we evaluate the mon DEQ instantiations described in Section 4 on several image classification benchmarks. We take as a point of comparison the Neural ODE (NODE) [8] and Augmented Neural ODE (ANODE) [10] models, the only other implicit-depth models which guarantee the existence and uniqueness of a solution. We also assess the stability of training standard DEQs of the same form as our mon DEQs. |
| Researcher Affiliation | Collaboration | Ezra Winston School of Computer Science Carnegie Mellon University Pittsburgh, United States EMAIL J. Zico Kolter School of Computer Science Carnegie Mellon University & Bosch Center for AI Pittsburgh, United States EMAIL |
| Pseudocode | Yes | Algorithm 1 Forward-backward equilibrium solving; Algorithm 2 Peaceman-Rachford equilibrium solving; Algorithm 3 Forward-backward equilibrium backpropagation; Algorithm 4 Peaceman-Rachford equilibrium backpropagation |
| Open Source Code | Yes | Code is available at http://github.com/locuslab/monotone_op_net. |
| Open Datasets | Yes | We train small mon DEQs on CIFAR-10 [17], SVHN [22], and MNIST [18], with a similar number of parameters as the ODE-based models reported in [8] and [10]. |
| Dataset Splits | No | The paper mentions training and testing, but does not provide explicit details about validation splits, percentages, or sample counts needed to reproduce the data partitioning. |
| Hardware Specification | Yes | All experiments are run on a single RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., programming languages, libraries, or frameworks like PyTorch, TensorFlow). |
| Experiment Setup | Yes | For further training details and model architectures see Appendix E. |